1510 results about "Serialization" patented technology

The invention relates to a network attack tracing method, system and device based on a user portrait, and a medium. The attack tracing method comprises the following steps: collecting interactive behavior data of a user in a Web page; performing compression and serialization processing on the interaction behavior data to generate a structured interaction log file; collecting fingerprint data of the user terminal and performing standardization processing to generate a fingerprint feature vector; based on the structured interaction log file and the fingerprint feature vector, generating a user unique identifier containing a risk level tag; performing clustering analysis on the interaction behavior data according to the unique identifier of the user, identifying an abnormal behavior mode of the abnormal user and marking an attack type; acquiring a public network IP address of an abnormal user, and positioning a geographic position in combination with an IP database; and constructing an attack traceability report based on the user unique identifier of the abnormal user, the abnormal behavior mode, the public network IP address and the geographic position. According to the invention, the user portrait can be accurately constructed, and the network attack behavior can be effectively identified and traced.

The invention provides a coal seam anti-reflection effect prediction method based on multi-source data fusion, and belongs to the technical field of machine learning prediction. According to the method, coal seam anti-reflection multi-source data is collected in real time, normalization processing is carried out on the data, then a coding mode is designed for different types of data, and key features are extracted; then performing unified serialization on the multi-source representation data, and aligning a time scale; a self-attention mechanism is adopted to calculate the time dependence weight of single-mode data, and the importance of different data sources is dynamically adjusted; and finally, capturing information complementarity of cross-modal data by adopting a cross attention mechanism. And inputting the fused data into the trained coal seam anti-reflection multi-parameter combined prediction model, outputting four key parameters including permeability, gas yield, pressure variation and fracture density, and performing prediction analysis on the coal seam anti-reflection effect. According to the method, comprehensive evaluation of the coal seam anti-reflection effect is achieved, compared with an existing method, the coal seam anti-reflection process can be described more accurately, and prediction precision is improved.

The invention belongs to the technical field of intelligent traffic system and artificial intelligence crossing, and particularly relates to a traffic multi-agent simulation decision-making method and system based on a large language model. Road network state data are coded into a three-dimensional feature matrix containing channel dimensions, time dimensions and space dimensions, and joint representation of numerical road network data and text event reports is achieved through a hybrid embedding model. The decision-making layer comprises a dynamic Prompt generator which generates a candidate scheme set based on a four-layer progressive prompt structure; the Monte Carlo tree search multi-objective optimization is executed in cooperation with the decision core module; and the execution layer comprises a cross-language communication bridging device which adopts a gRPC bidirectional stream communication protocol and a Protobuf data serialization scheme to realize millisecond-level data interaction between services and support a hot plug mechanism of a strategy injection interface. According to the invention, dynamic optimization of urban traffic resource allocation and breakthrough improvement of simulation deduction efficiency are realized.

The invention discloses a multi-source heterogeneous database SQL (structured query language) statement sensitivity calculation method, relates to the technical field of SQL query, and aims to accurately calculate and add noise of a proper magnitude in a complex SQL statement. The adopted scheme is as follows: receiving and preprocessing an SQL query request, and disassembling the SQL query request to generate an abstract syntax tree; traversing the abstract syntax tree, identifying syntax features needing to be processed, defining a rewriting rule, and modifying and optimizing corresponding nodes in the abstract syntax tree; selecting a serialization format, and converting the optimized abstract syntax tree into corresponding serialization data; and performing semantic analysis and logic analysis on the serialized data, binding semantic information contained in the request with metadata information of a target database, calling a sensitivity evaluation algorithm, and performing sensitivity calculation on the request. The sensitivity obtained through calculation is used for guiding data noise addition in an SQL query result.

The invention discloses a method and a system for supporting visual and AI bidirectional intercommunication editing views. The method comprises the following steps: establishing a bidirectional mapping rule between a rendering tree protocol and an intercommunication protocol; in response to an operation of a user on the visual editor, generating a first atomic operation instruction, applying the first atomic operation instruction to the current rendering tree and triggering local redrawing of a view; serializing the updated rendering tree into context segments in an intercommunication protocol format according to the mapping rule, and pushing the context segments to an AI model; responding to a natural language editing request initiated by a user to the AI model, receiving streaming data in an intercommunication protocol format returned by the user based on the request, analyzing the streaming data according to the mapping rule to obtain a second atomic operation instruction, applying the second atomic operation instruction to the rendering tree and triggering local redrawing of a view, and obtaining a second atomic operation instruction. The view is edited by the AI, so that real-time two-way intercommunication between visual editing and AI editing is realized, and the user experience is remarkably improved.

The invention belongs to the field of high-performance computing, and relates to an integer parallel computing method and device based on distributed storage and computer equipment, and the method comprises the steps of collecting real-time resource indexes, dynamically identifying fault nodes, triggering task migration, and performing data verification and hard disk fault detection. The weight value of each node is calculated, the nodes are arranged according to the descending order of the weight values, and the nodes with high load capacity are selected to distribute tasks; dynamically distributing a data generation task to a computing node, executing parallel computing, and performing distributed storage on a result; obtaining an operand, converting the operand into a first-order tensor form of a basic operand, serializing tensor data, and sending the serialized tensor data to a parallel computing layer; distributing a search task to a computing node, retrieving storage data in parallel, reading effective data from a storage layer, and combining search results into a partial sum; and summarizing and then outputting. The system has dynamic resource management and fault-tolerant capabilities, and can realize efficient task allocation and load balancing.

The invention discloses a water conservancy multi-modal intelligent decision-making method based on a model association protocol, and the method comprises the following steps: obtaining hydrological, meteorological and remote sensing data, and carrying out the preprocessing and feature extraction respectively; performing structured coding on the extracted multi-modal features by using a data serialization protocol to generate corresponding serialization codes, and performing packaging and transmission by using an MCP protocol; at a receiving end, de-encapsulation is carried out by using a corresponding MCP protocol, the data is restored to original multi-modal features, and each modal feature is fused through a dynamic weight distribution mechanism and a cross-modal attention mechanism; inputting the fused features into a big decision model driven by a knowledge graph, and generating a flood risk level and a corresponding decision through mixed decision reasoning by means of the knowledge graph and a rule engine; according to the method, the problem of data islands is solved, efficient fusion of multi-source data is realized, the decision process is more intelligent and interpretable, and the flood risk level and the prediction accuracy of the corresponding decision are improved.

The invention provides a transformer substation operation and maintenance-based lock control terminal work log query method and system, and the method comprises the steps: collecting an original operation log stream generated by a transformer substation lock control terminal in a preset time period, serializing the original operation log stream to obtain a log data sequence, and then carrying out the context correlation analysis and recognition of an entity interaction relationship and behavior attribute description in a log text; a structured log metadata set is obtained after standardized integration; constructing a spatial topological index based on the physical layout and the electrical connection relationship of the substation equipment, mapping the equipment associated information in the spatial topological index to a corresponding node position of the spatial topological index, and generating a spatial enhanced log data set; receiving a natural language query request, generating a structured query expression, performing multi-dimensional retrieval matching on the spatial enhancement log data set, and outputting a preliminary query result set; and carrying out time sequence correlation analysis and operation influence evaluation on the log records to generate a query report. According to the invention, efficient and accurate query of the work log of the transformer substation lock control terminal can be realized.

The invention discloses a high-resolution remote sensing image semantic segmentation method based on multidirectional parallel selective scanning, and belongs to the technical field of high-resolution remote sensing image processing. According to the method, a multidirectional parallel selective scanning model is provided, direction perception modeling is carried out through 8-direction serialization scanning in combination with a state space model SSM, and the multidirectional long-distance dependence capture capability is enhanced while the linear calculation complexity is kept. A pyramid encoder-decoder structure is constructed, multi-level feature extraction is realized through a four-stage OSSBlock module, and local details and global semantics are dynamically fused in cooperation with SE attention jump connection of a decoder. A selective scanning mechanism is adopted to replace self-attention, and linear complexity calculation is realized through a state space parameter matrix; and designing a mixed loss function, and improving the small target segmentation precision in combination with the class balance of Dice Loss and the difficult sample mining capability of Focal Loss.

The embodiment of the invention provides a method and a device for processing a layout. The method comprises the following steps: receiving a plurality of OASIS file blocks respectively corresponding to a plurality of layout blocks of the layout; and merging the plurality of OASIS file blocks to generate an OASIS file corresponding to the layout. In this way, deserialization processing on the layout data on the main node and OASIS format conversion processing on all the layouts can be avoided, and therefore use of computing resources of the main node is reduced. In addition, merging processing on the original layout blocks on the main node can be avoided, and use of memory resources is reduced. In addition, layout data transmission can be carried out between the working node and the main node in an OASIS format, and the data volume of network transmission is reduced.

The invention relates to a hyperspectral image classification method and device based on a state space model. The hyperspectral image classification method based on the state space model comprises the following steps: sequentially carrying out feature extraction and serialization processing on hyperspectral image data to obtain a shallow feature projection vector; performing global-local feature extraction on the shallow feature projection vector by adopting a neural network based on a state space model to obtain a fused feature projection vector; and carrying out pixel-by-pixel classification and dimension rearrangement on the hyperspectral image data in sequence to generate a classification result of the hyperspectral image data. According to the hyperspectral image classification method based on the state space model, long-range dependence modeling is achieved through the neural network based on the state space model with linear complexity, the calculation complexity is effectively reduced, and through feature fusion and residual error connection, the classification accuracy of the hyperspectral image is improved. And the perception capability of the neural network on different scale space-spectrum structures in the hyperspectral image is effectively enhanced.

The invention belongs to the field of three-dimensional target detection, and particularly relates to a lightweight Mama three-dimensional target detection method based on columnar point cloud representation, which mainly consists of three parts: a spatial feature processing module based on Hilbert serialization and dynamic grouping, a Mama module based on a selective state space and a lightweight pyramid feature fusion module. The Hilbert serialization module keeps spatial locality through a serialization mode, and is helpful for capturing a structural relationship between adjacent points in the point cloud, so that the modeling effect of local feature integration and context information is improved; the Mama module adopts an input-dependent dynamic modeling mechanism, can provide higher modeling efficiency and stronger long-range dependent modeling capability when processing a large-scale volume column sequence, and is particularly suitable for a sparse point cloud scene; the lightweight pyramid fusion module enhances the perception capability of a target boundary through single-round up-down sampling and feature fusion, and improves the detection performance of a small target, thereby effectively balancing the efficiency and precision of 3D target detection.

The invention provides a shield muck three-dimensional point cloud segmentation and volume calculation method based on deep learning, and belongs to the technical field of deep learning and shield, and the method comprises the steps: S1, point cloud data collection, preprocessing and sample labeling; s2, constructing and training a deep learning segmentation model; s3, carrying out muck point cloud segmentation; and S4, calculating the volume of the muck. According to the method, efficient interaction of point cloud attention is realized by adopting four serialization modes, and the influence of point cloud disorder on calculation is avoided.

The invention provides a reward model training method and device, a strategy model training method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining a preference data pair which comprises a preferred response and a non-preferred response generated for the same prompt word, and each of the preferred response and the non-preferred response is composed of a plurality of text unit sequences; inputting each text unit sequence into a to-be-trained reward model to obtain a predicted reward value; and calculating the total training loss according to the predicted reward value, and updating the model parameters of the to-be-trained reward model. According to the method, the response text is subjected to serialized splitting, and the preference data composed of the preferred response and the non-preferred response is introduced for comparative learning, so that the target of model training is no longer to evaluate the absolute quality of a single response, but to identify a key text unit which causes one response to be superior to the other response; the fine-grained evaluation of the response content is realized, and the evaluation accuracy of the reward model and the identification capability of complex user preferences are effectively improved.

The invention discloses a PCB component layout method, device and equipment based on a multi-modal large model, and relates to the technical field of electronic design automation, the method comprises the following steps: carrying out quality screening on a plurality of PCB design files which have been laid out to obtain a qualified design file set; constructing a serialized training sample set based on the qualified design file set; each training sample sequence comprises a plurality of training samples arranged according to a layout logic sequence; each training sample comprises multi-modal data representing a current layout state and a real position coordinate of a next component to be laid out; based on the serialized training sample set, training the open-source multi-modal large model to obtain a PCB component layout model; and iteratively generating a layout file of the target PCB based on the netlist file of the target PCB and the PCB component layout model. The method is high in generalization capability and low in cost based on the PCB component layout model trained by the layout data, and can realize rapid layout of components of a new PCB.

The invention provides a dynamic traceability identifier generation and verification method based on multi-node environment data fusion, and relates to the technical field of information security and Internet of Things data credibility. The method comprises the following steps: firstly generating a seed identifier, then collecting environment data through each node to construct a multi-dimensional vector and carrying out weighted fusion, after serialization, generating a dynamic hash identifier by combining with an upper-level identifier, and meanwhile, introducing an offline trusted evolution, time drift tolerance and self-adaptive risk control mechanism; and finally, backtracking the traceability chain through recursion verification, and guaranteeing privacy in combination with zero-knowledge proof. The method solves the problems that a static traceability code is easy to copy and the data credibility is low, achieves the dynamic anti-counterfeiting and tamper-proofing of the traceability identifier, enables the digital identifier to be strongly associated with physical circulation, improves the credibility of the traceability process, can automatically find abnormal circulation and give an alarm, and is suitable for a commodity traceability scene.

The invention discloses an internal threat detection method based on user classification and behavior feature analysis, which comprises the following steps of: separating log files to be processed according to different users, and arranging the log files of each user into a format convenient for subsequent operation processing according to time and sources; performing risk identification on the log file content of the user, and extracting behavior statistical characteristics and user behavior serialization characteristics of the user; clustering all users based on the extracted user behavior statistical features and user behavior serialization features, and extracting class features of each user class; and identifying potential abnormal risk behaviors by using the features generated by current log processing of the user and the features of the category to which the user belongs, and detecting the internal threat risk caused by the abnormal risk behaviors of the user. According to the method, the deployment and maintenance cost of the detection framework is greatly reduced, internal threat detection can be carried out by using a lower-cost detection technology, and the method has a relatively high detection rate.

The embodiment of the invention provides a communication method and device based on graphic modeling, the historical protocol features are analyzed through a deep learning model, the protocol template data set is generated, and the intelligence of protocol design is realized. A graphical protocol design interface is innovatively designed, a communication protocol general block and a logic block are integrated, and a compatibility parameter matrix is introduced to realize intelligent connection correction. The system automatically generates a protocol analyzer, and network data streams are processed through a serialization function library and an algorithm function library, so that the accuracy and reliability of data transmission are ensured. According to the method, the limitation of traditional manual coding is broken through, and an efficient and intuitive visual solution is provided for communication protocol development.

The invention belongs to the technical field of computers, and particularly discloses a method and system for integrating a code structure into a large model based on multiple modalities, and the method comprises the steps: obtaining a code attribute graph corresponding to a code text; inputting the code attribute graph to a graph encoder, and obtaining a graph structure representation output by the graph encoder; the code text and the graph structure representation are input to a large language model, a code processing result output by the large language model is obtained, and the large language model is used for processing the code according to the code processing task based on the code text and the graph structure representation; wherein the graph structure representation and the serialization language representation are kept aligned in a representation space, and the serialization language representation is obtained by performing serialization processing on a code text by a text encoder in a large language model. According to the method, the multi-modal encoder is introduced, so that different modal information, such as grammar, semantics, control flow and data flow, of the code can be fused and processed, and the understanding ability of a large model to a code structure can be remarkably improved.

The invention relates to the technical field of environment monitoring, in particular to an electric power carbon emission real-time monitoring system, which comprises a data acquisition module, a data processing module, a data processing module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, wherein the data processing module receives sensor data of a plurality of monitoring points of a power plant and comprises a carbon emission value, a timestamp and an environment parameter of each sensor; and sorting according to the time sequence characteristics of the data, and establishing a serialized data set. According to the method, data cleaning and time serialization processing are applied, data accuracy and availability are improved, data management is more adaptive and prospective by dynamically adjusting calculation of strategies such as kurtosis and skewness, geometric and topological analysis methods are introduced to improve understanding of data structure features, complex modes in data are effectively recognized, and the method is suitable for being popularized and applied. According to the method and the system, the dependency relationship of a hole, a cluster and an emission source and the application of an anomaly detection technology are applied, so that timely identification of abnormal behaviors is enhanced, potential equipment faults or operation errors are allowed to be found in an early stage, timely intervention is realized, and unnecessary environmental influence is reduced.

The invention discloses an intelligent twin regulation and control method and system based on material process optimization. A multi-node regulation and control mechanism is set along a time axis, and equipment parameters, process parameters and environment parameters are collected and serialized into a multi-dimensional input sequence; synchronously acquiring spectral characteristics and performance indexes of the material, and constructing a spectrum-performance output sequence; establishing a many-to-many parameter simulation model based on a Transform architecture, and analyzing a dependency relationship among parameters by using a multi-head attention mechanism; a spectrum-efficiency data set is extracted from literatures in combination with a semantic model, prediction analysis and optimization parameter setting are carried out on real-time / simulation parameters, finally process adjustment is guided through prediction results and parameters, and a closed-loop feedback iterative optimization simulation model is formed. According to the method, material process dynamic modeling, precise prediction and regulation and control analysis are realized, the process optimization efficiency and the material performance prediction precision are remarkably improved, and the research and development trial and error cost is reduced.

The invention discloses a PDF contract file identification method and system, a medium and a program product, and relates to the technical field of information identification, and the method comprises the steps: carrying out the image analysis of an obtained PDF contract file through employing a deep learning model, generating an editable text, and synchronously identifying the page layout of the document, outputting structured data including page numbers, text paragraphs and coordinate information; performing multi-dimensional feature matching on the text paragraphs according to an adaptive semantic analysis algorithm, a preset contract template library and a dynamic keyword library, and positioning contract core element information; performing entity relationship verification on the content of the contract core element information, correcting an extraction error through dependency syntactic analysis and semantic role labeling, and establishing a contract element data set containing a confidence coefficient weight; and performing serialized packaging on the verified contract element data set to generate a contract element list conforming to electronic signature authentication. According to the invention, the processing efficiency and recognition precision of the PDF contract file are improved.

Data structures and applications are defined with an application development environment which employs the use of a natural language artificial intelligence interface and recorded metadata descriptions, which are converted at runtime to generate underlying software code (e.g., C#, SQL, JavaScript, etc.) to implement the application. The data structures include application data elements described in a data serialization format. Each application data element includes fields that each represent an individual value. Each field is defined with a label, a data type, and attributes. The data structures also describe relationships or links between application data elements, as well as data-driven or time-based rules that drive further data manipulation and interactions within and external to the programming development environment through a variety of interfaces (e.g., HTTP, SMTP, SMS).

The invention provides a multi-modal content generation AI model construction method and device based on a diffusion model, and relates to the technical field of content generation. The method comprises a multi-source data processing module, a modal priority analysis module, a serialized modal processing module, a diffusion parameter adaptive adjustment module, an interactive feedback integration module and a collaborative output adjustment module. According to the method, multi-modal data input is integrated, model parameters are dynamically adjusted, information entropy is used for evaluating the information amount of the data, the processing priority is set according to the information amount, the recognition capacity of the model for data importance is enhanced, the model parameters are adjusted in real time to adapt to the characteristics of different data modalities, and the accuracy of data processing is improved. And the generation parameters are dynamically adjusted through user feedback, so that the generated content better fits the actual demand and preference of the user, and the consistency and integration of the content are ensured, the attraction of the content is improved and the propagation effect of the content is enhanced by coordinating the style, theme and emotion of the output content.

A computer-implemented method and apparatus for managing physical and digital products' customization using container format are disclosed. In some embodiments, the method comprises receiving a container file with serialized objects, deserializing these objects to form a hierarchical object structure, and interacting with a database to obtain real-time configuration data. Custom attributes of the products are defined by binding attribute / value pairs to the hierarchical structure. A visualization of the customized product is generated based on these pairs, and manufacturing instructions are determined considering the attribute / value pairs and manufacturing constraints. The apparatus comprises a memory and a processor to execute instructions for deserializing the container file, retrieving configuration data, organizing objects into a hierarchical tree, negotiating product customizations, and outputting manufacturing data. This system enables efficient and accurate production of customized products by managing data, rendering graphics, and incorporating manufacturing constraints.

The invention relates to the technical field of artificial intelligence and automatic testing, and particularly discloses an AI-based automatic testing method and device. The method comprises the following steps: capturing and recording complete execution historical information in a process of executing a test task by an AI agent, and serializing and storing the complete execution historical information as reusable cache data; before the test is executed, the validity of the cache is judged through a multi-dimensional verification mechanism including data integrity, timeliness, functionality and the like; when the cache verification is passed, a result is quickly obtained based on the cache data playback test process; when cache verification is not passed or execution fails, triggering an AI agent to perform new reasoning and steps to generate and update cache data; and meanwhile, a caching strategy is dynamically adjusted according to test case changes and resource conditions. The device comprises a test executor, an AI agent manager, an intelligent cache manager and a dynamic cache management module, and all the modules work cooperatively to improve the test efficiency, reduce the calling cost of a large language model and enhance the stability of a test result. The method is suitable for Web / APP automatic testing, cross-platform testing, CI / CD process testing and other scenes, the execution efficiency can be remarkably improved, the cost is reduced, and resource utilization is optimized.

The invention discloses a distributed job scheduling method and device, a storage medium and computer equipment, relates to the technical field of distributed task scheduling, is suitable for the field of financial and medical services, and mainly aims at solving the problem that an existing distributed job scheduling system lacks task dependence management and dynamic scheduling capability. Comprising the following steps: performing task modeling processing on each to-be-executed task by adopting a binary tree structure to obtain a task binary tree corresponding to each to-be-executed task; performing serialization processing on the task binary tree by adopting a job arrangement engine to obtain a corresponding task sequence; issuing the task sequence to an event flow bus; after the event flow bus is started, tasks to be executed are allocated to different distributed actuators for execution based on the central control scheduler; and the distributed executor returns an execution result to the event flow bus in real time, so that the event flow bus triggers the downstream task after the upstream task is completed until the task sequence is completely executed.

The invention discloses an efficient code feature extraction method suitable for different programming languages, which comprises the following steps: loading different programming language configuration files from a specified project path, analyzing the language configuration files through a predefined language analysis rule mapper, and generating a language rule mapping table suitable for different programming languages; identifying programming language types, creating a multi-task queue according to the types and distributing task threads; preprocessing the source code according to the language rule mapping table, performing word segmentation and marking, and normalizing a word segmentation result to generate a normalized source code file; performing code block segmentation on the normalized source code file according to a given window and step size, generating hash values as characteristic values, and obtaining a code file taking a hash value sequence as a characteristic fingerprint; and carrying out serialized storage and output on the code file taking the hash value sequence as the feature fingerprint and the marked content according to a standardized format. According to the method, the code analysis efficiency and accuracy can be improved.

The invention discloses a multi-dimensional data asynchronous calculation method and system based on a dynamic dependency graph, and relates to the field of data processing. The method comprises the following steps: constructing a metadata, logic and instance three-layer separation storage model; analyzing the reference relationship to construct a directed acyclic graph, and generating a calculation priority; monitoring data change, and executing asynchronous serialization calculation through a message queue based on priority; the associated document is automatically updated based on anchor mapping. According to the method, the calculation deadlock and the performance bottleneck of large-scale data in the Web environment are solved, logic decoupling and dynamic expansion are realized, the final consistency of the data is guaranteed, and the high-concurrency throughput and the stability are remarkably improved.