666 results about "Serialization" patented technology

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 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 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.

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.

The invention belongs to the technical field of vehicle software testing, and particularly relates to a test case generation method and system based on big language model and rule collaboration, and the method comprises the steps: analyzing a demand document, and mapping the demand document to a fault ontology model to generate a structured test demand; generating a plurality of groups of fault verification conditions based on equivalence class division and a boundary value analysis method by utilizing a large language model; a multi-dimensional mixed retrieval strategy is adopted to retrieve a reference case from a historical case library; in combination with the verification condition, the reference case and the standardized information base, preposed environment settings, serialized operation instructions and expected responses of the test case are generated in stages; and finally, the case quality is ensured through cooperative verification of a large language model and a rule engine. The problems that a traditional method depends on manpower, the coverage rate is low, errors are prone to occurring, and historical knowledge is difficult to reuse are effectively solved, and the automation degree, accuracy and integrity of BMS test case generation are remarkably improved.

The invention relates to the field of machine learning, particularly discloses an artificial intelligence-based power system fault analysis and diagnosis method, and effectively solves the problem of information loss caused by neglecting a key waveform form in a transient signal in the prior art through a local feature extraction and serialization module. An original signal is converted into a local feature sequence with more characterization significance. Aiming at the averaging bottleneck of an existing model in an information aggregation stage, a traditional feature compression method is abandoned, and a sequence information aggregation and decision-making mechanism is provided. According to the mechanism, a context sensing sequence is regarded as a probability event, and modeling is carried out on the sequence from three orthogonal dimensions of a content center, time sequence dispersion and distribution uncertainty by calculating feature expectation, time sequence variance and information entropy of the context sensing sequence. The method can deeply insight and quantify the essential difference of different events in the time sequence dynamic evolution mode, thereby fundamentally solving the problem of misjudgment caused by feature confusion.

The invention discloses an intelligent extraction method and equipment for structural data of a scanned copy and a medium, and relates to the cross technical field of computer vision and intelligent document processing. The method comprises the following steps: acquiring a to-be-processed scanned copy image, and performing joint feature extraction on the scanned copy image based on a preset multi-modal neural network model to generate intermediate feature representation; constructing an extensible structured index template library containing field rules, value domain constraints and cross-field verification logic; based on an attention mechanism, performing intelligent semantic matching and mapping on the intermediate feature representation and a target field in an extensible structured index template library to output preliminary structured data; based on a verification rule preset in an extensible structured index template library, executing automatic logic verification and conflict resolution on the preliminary structured data to generate standard structured data conforming to business specifications; and carrying out serialization output on the standard structured data according to a predetermined format.

The invention provides a structured memory data processing method and system oriented to long time sequence interaction, and is applied to the technical field of natural language processing and artificial intelligence memory modeling. The method comprises the following steps: acquiring a real-time interaction data stream, partitioning the real-time interaction data stream into dialogue data blocks, loading a hierarchical attribute mode and a previous time step attribute tree instance, inputting serialized texts of the hierarchical attribute mode and the previous time step attribute tree instance into a generative model, generating a writing, rewriting, deleting or null operation instruction aiming at a leaf node path, analyzing, updating, generating a current attribute tree instance, and storing the current attribute tree instance; by means of the scheme, lossy compression and structured evolution of the infinite long dialogue stream can be achieved, memory forgetting is relieved on the premise that a context window is not expanded, and long-time-sequence information retrieval precision and storage efficiency are improved.

The invention discloses an end-to-end lightweight road crack sensing method, system and device and a medium, belongs to the technical field of computer vision and deep learning, and aims to solve the technical problem of how to quickly and accurately detect road cracks and improve the road crack detection efficiency. According to the technical scheme, the method comprises the following steps: image standardization processing: carrying out standardization preprocessing on a collected road image to obtain a road image after standardization preprocessing; multi-scale dynamic labeling: labeling the road image after standardization preprocessing by adopting a multi-scale dynamic labeling mechanism to construct a training label; light-weight road crack perception: based on an improved DETR model, introducing a cross attention mechanism into a Transform encoder to improve the perception ability of crack local features, and adopting a serialization interaction mechanism in the Transform encoder to realize crack integrity reasoning; and transfer learning incremental training: improving the generalization performance of the model through a transfer learning strategy combining public data set pre-training and own data set incremental training.

The invention relates to the technical field of computers, in particular to a block chain power abnormal data tracing method. The method comprises the following steps: acquiring operation data and system logs of a power system, extracting model input, identifying exceptions and completing event source classification; for the abnormal generation differential change records, timestamps are extracted to construct chain records, and the chain records are serialized into traceable paths; performing cross-source alignment and sequence correction to form a consistent event sequence, classifying and sorting to obtain an event sequence structure, and verifying to generate a cross-system traceability path; and finally verifying a conclusion through a consensus mechanism, extracting a traceability report, and updating the anomaly detection model according to the traceability report. According to the method, the cross-source time sequence consistency and the evidence playback performance are improved, the conclusion credibility and the closed-loop iteration capability are enhanced, and the method is suitable for scenes such as data metering and equipment monitoring.

The invention relates to the technical field of browser-side high-performance computing, in particular to a WebAssembly-based high-performance front-end audio and video processing method and system, and the method comprises the following steps: designing a modularized Wasm runtime container, constructing a zero-overhead memory interaction mechanism, and establishing a hardware adaptive scheduling engine; the method has the beneficial effects that dynamic loading of an audio and video algorithm is realized by designing a modularized Wasm runtime container, serialization overhead of communication between threads is eliminated by utilizing a shared memory mechanism, a hardware self-adaptive dynamic scheduling system is created, and finally three core objectives are achieved: execution efficiency close to native codes is provided in a pure browser environment; establishing a standardized processing pipeline without dependence of a third party; intelligent scheduling and elastic expansion and contraction of terminal heterogeneous computing resources are realized, and Web end infrastructure support is provided for professional audio and video applications.

The invention discloses a report generation agent method, system and device driven by a large language model and a medium, and the method comprises the steps: constructing an agent execution framework independent of a native interface, outputting a decision through a cue word constraint model in a JSON structure, and scheduling a local tool function; analyzing the reference data and constructing a local vector knowledge base supporting semantic retrieval; the intelligent agent autonomously plans and generates a report directory structure based on a local vector knowledge base, stores the report directory structure as a structured intermediate file as a persistent intermediate representation, generates chapter content in parallel by utilizing a concurrent flow control and exponential backoff retry mechanism and combining a mixed retrieval strategy, and serializes and updates the chapter content to the intermediate file in real time; and finally, converting the intermediate file into a target format report according to a mapping rule. The system, the equipment and the medium are used for implementing the method. According to the method, the efficiency and the accuracy of complex report generation are remarkably improved through agent whole-process autonomous driving and combination of persistent intermediate representation, retrieval enhancement and concurrency control technologies.

The invention discloses a PDF engineering drawing cross-end synchronization method and system based on operation instruction serialization and a storage medium, and belongs to the technical field of cross-end engineering drawing cooperative process.The method comprises the steps that operation instructions of multiple ends on a PDF engineering drawing are captured, and operation types, target object IDs and parameters are extracted to serve as semantic metadata; constructing a dependency graph of the operation instruction based on the target object ID, and identifying a key path instruction; encoding an operation instruction and a dependency relationship into a serialized format, and only transmitting an instruction of a changed part; the receiving end analyzes the serialized instruction, detects conflicts based on semantic metadata, and executes the instruction according to the dependency relationship; and dynamically adapting the rendering result according to the equipment parameters, including coordinate system conversion and symbol mapping. Therefore, the problems of synchronization efficiency and accuracy in the prior art are solved, and the effects of multi-terminal real-time, efficient and synchronous consistency of the engineering drawing are achieved.

The invention relates to the technical field of data synchronization, in particular to an incremental synchronization and verification method and system for an access control authorization list, and the method comprises the steps: initializing a terminal and a server; the server side obtains the list change data to progressively increase the version number, and pushes the version number to the terminal equipment; the terminal initiates an incremental request to the server according to the notification message and obtains a server response incremental data block; the terminal performs multi-factor data verification on the incremental data block; performing cyclic synchronization on the incremental data; the terminal performs full-amount consistency verification on the incremental data; server real-time message notification and long connection transmission are adopted to replace a traditional polling mechanism, and the millisecond-level response speed is achieved; the rpcx protocol is matched with a proto binary format to perform data transmission, so that the protocol header overhead is reduced, and the serialization efficiency is improved; a multi-factor data verification mechanism is introduced, list number verification, incremental version number continuity verification and full-text hash verification are combined, a multi-level data consistency guarantee system is constructed, and the problems of data missing and wrong sending are avoided.

The invention belongs to the technical field of artificial intelligence, and relates to an LSH-based retrieval enhancement generation method and device, equipment and a storage medium thereof. Carrying out serialization processing; sampling processing is carried out through the vector space model, and high-dimensional embedded vector representation is generated; inputting the high-dimensional embedded vector representation into a vector database, and carrying out matching calculation; carrying out binary code valuation conversion on a matching calculation result by adopting an LSH algorithm to obtain binary code values corresponding to all the retrieved texts after conversion; according to the binary code value, an expected retrieved text is screened out; combining and sorting the retrieval input text and the expected retrieved text to generate a to-be-enhanced text; and inputting the text to be enhanced into the text enhancement generator to generate an enhanced retrieval feedback text. The method is applied to webpage search, question and answer systems and recommendation system scenes in the field of financial services or medical services, and a faster and more accurate retrieval feedback result is provided for retrieval personnel.

The invention belongs to the technical field of enterprise informatization management and big data integration, and particularly relates to an enterprise informatization management integration platform based on big data. Comprising an auditing element unified structured acquisition module, a main body unique identifier analysis and failure closed loop module, a caliber version and field mapping version joint locking module and an evidence index binding and consistency verification packaging module. The platform reads interface and field mapping from a service system log according to a configuration table, normalizes the interface and field mapping and writes the interface and field mapping into an event account book collocation state; executing main body gating on the original entry record to generate a main body identifier and writing back the main body identifier; when failure occurs, the reason code is written and blocked, and the failure evidence index is returned; performing node analysis on the aperture script and generating an aperture chain fingerprint; and serializing the event segments, calculating abstracts, constructing batch abstract roots, positioning first inconsistent serial numbers when recalculation is inconsistent, and scheduling and supplementary collection, so as to output a recheckable audit evidence packet. According to the invention, aperture consistency and evidence traceability can be realized.

The invention belongs to the technical field of virtual reality, and particularly relates to a virtual-real fusion scene automatic construction method based on AIGC script generation, which comprises the following steps: step 1, inputting a script text input by a user into a large language model for structured analysis, generating a corresponding three-dimensional geometric grid, a surface texture map and an initial placement position according to the updated feature vector of each node; 2, collecting an eye image sequence of a user through an eye movement tracking module, and executing boundary fusion processing on rendering results of all rendering areas to output adaptive rendering frames; and step 3, performing collision detection and iterative position correction on each three-dimensional geometric grid, and performing assembly and serialization export on each corrected three-dimensional geometric grid. According to the invention, end-to-end automatic construction from the script text to the interactive virtual-real fusion scene is realized.

The invention discloses an inspection task configuration method and system based on a mandatory behavior interface and a structured scene file, and the method comprises the steps: providing functional module items which forcibly achieve a unified behavior interface in a graphical designer, and enabling a user to form an inspection flow chart through dragging; writing the flow chart into a structured scene file through a serialization method defined by an interface; the emulator and the deployment tool load the file respectively, function module items are reconstructed through deserialization, an execution method of the function module items is directly called for logic verification, or robot instructions are generated through analysis. According to the method, a unified interface is used for binding graphs and logics, a single scene file is used for connecting the whole process of design, simulation and deployment, the problems that in a traditional scheme, operation is complex, and data splitting and semantics are inconsistent are solved, the purpose that line is achieved through programming is achieved, the configuration threshold is remarkably lowered, and development efficiency and system reliability are improved.

The invention relates to the technical field of data processing, in particular to a financial contract online editing system based on multi-person collaboration, and the method comprises the steps: capturing an operation feature vector with a space-time mark in real time through an acquisition unit; the analysis unit generates a regional operation intention set in combination with the contract semantic tag library; the conflict probability calculation unit fuses the space overlapping degree, the logic association chain and the time sequence overlapping window to quantify the conflict risk; the dynamic partition response unit triggers area locking, operation confirmation or background monitoring according to probability value grading; the legal rule verification unit implements compliance interception and outputs a forced interrupt signal; the business logic serialization unit reconstructs an instruction sequence; the multi-strategy synchronous execution unit carries out atomic broadcasting, transaction binding and batch asynchronous updating by differentiating a core term channel, a dependency group channel and a common term channel; and the closed-loop audit tracking unit aggregates full-link data to generate a verifiable influence chain. The risk of data inconsistency caused by concurrent operation of financial contracts is systematically solved.

The invention provides an intelligent Internet of Things safety monitoring method and system, and the method comprises the steps: obtaining an equipment operation record and a behavior operation record, carrying out the event serialization coding of the equipment operation record, obtaining a sensor event sequence vector, carrying out the behavior mode vectorization processing of the behavior operation record, obtaining a behavior event sequence vector, and carrying out the monitoring of the behavior event sequence vector. Inputting the sensor event sequence vector and the behavior event sequence vector into a pre-trained association analysis model, carrying out cross-sequence causal relationship mining processing, generating an abnormal event association graph, and based on event node attributes and association edge weight values in the abnormal event association graph, executing risk conduction path analysis to obtain a risk conduction path; and obtaining a key conduction path set and a risk accumulation intensity value of the house safety risk, and generating a safety monitoring result containing the risk level identifier and the risk source positioning information according to the key conduction path set and the risk accumulation intensity value. According to the invention, the accuracy and comprehensiveness of intelligent internet-of-things safety monitoring are effectively improved.

The invention discloses a serialization-based point cloud over-segmentation method, device and equipment and a medium, and relates to the technical field of point cloud over-segmentation. The point cloud over-segmentation method comprises the following steps: obtaining point cloud data set preprocessing; serializing the preprocessed point cloud data into a Hilbert curve; dividing the serialized point cloud data into a plurality of initial segments based on spatial continuity, and obtaining a multi-scale segment set; the feature similarity between adjacent segments is calculated, and a similarity matrix is obtained and established to provide a basis for subsequent super-point clustering; carrying out super-point clustering on the initial segments based on a self-adaptive updating algorithm; updating super-point features through a cross attention mechanism, establishing dynamic association between points and super-points, and obtaining a new super-point structure; and according to the updated super-point features, constructing a super-point graph, performing enhancement processing through a graph convolutional network, fusing features from the backbone point cloud network and corresponding multi-level super-point features, then transmitting the fused features to a segmentation head for segmentation, and obtaining semantic segmentation output.

The invention provides a hotspot technology mining method and system based on scientific and technological literature data, and relates to the technical field of scientific and technological information mining, and the method comprises the steps: firstly obtaining a scientific and technological literature data set containing papers and patent literatures, and constructing a current technical element network through technical phrase extraction and semantic normalization processing; dividing the technical element network segment into a time-serialized technical element network segment set according to time slices, calculating nodes and edge time sequence importance indexes to judge an emerging technical cluster, carrying out dynamic graph representation learning and future associated edge prediction on the set, generating a potential technical combination prediction set, and evaluating the potential technical combination prediction set; and finally, generating an emerging technology theme list and a technology fusion direction list in combination with an emerging technology cluster node growth trend and a prediction association edge evaluation result, thereby improving the accuracy, comprehensiveness and foresight of hotspot technology mining.

One example method is for constructing and pre-training a model of an information technology (IT) infrastructure, and includes creating a model of the IT infrastructure by, generating a KG (knowledge graph) representation of IT infrastructure data, serializing information flows within the KG to generate serialized information flows, and pre-processing the serialized information flows to improve a quality of the IT infrastructure data relative to a quality of the IT infrastructure data prior to the pre-processing, and pre-training the model, including training the model to capture a structure of both natural language and IT infrastructure flow by predicting, without hallucinations, tokens and identities of the IT infrastructure, providing customizations to enable the model to correctly predict the identities, and pre-training the model with causal modeling when identities are unknown in one of the information flows.

The invention relates to the technical field of data processing, particularly provides a data acquisition system, method and equipment based on an authorized webpage resource access level and a medium, and aims to solve the problem of low network data processing efficiency. In order to achieve the purpose, the data acquisition system comprises a resource access level acquisition module used for acquiring a resource access level based on webpage response data and webpage serialization characteristics of an authorized target webpage, and a data acquisition module used for acquiring the resource access level if the resource access level is greater than a preset access level. If yes, generating a robot process automation data collection script based on the webpage response data, the webpage serialization features and the resource access level, and obtaining target data of the authorized target webpage based on the robot process automation data collection script; if the resource access level is smaller than or equal to the preset access level, the target data of the authorized target webpage is obtained based on the automatic browser frame and / or the preset data collection interface, and the optimal collection scheme can be dynamically selected.

The invention provides a data serialization processing method, a data deserialization processing method, a medium and equipment, and the serialization processing method comprises the steps: receiving a target object, and reading a previous-level field path under an initial condition; obtaining the type of the target object, and determining whether the target object is empty or not; after determining that the target object is not empty, processing the target object according to the type of the target object to generate a corresponding key value pair, and putting the key value pair into an initialized key value pair list to generate a target key value pair list; wherein the key value pair comprises a key value and a value value; according to the method, a complex object is converted into a simple multi-row and two-column key value pair, so that a structure is conveniently and quickly read, the method is suitable for storage of a relational database, search and analysis can be realized by directly utilizing functional characteristics of the relational database, and the data search and analysis efficiency is improved.

The invention provides an electric power communication SDH network operation state real-time monitoring system and method, and the system comprises a multi-protocol adaptive collection module which carries out the collection of network element information, performance information, alarm information and equipment information of an electric power communication SDH network according to a preset strategy, performing standardization processing on the acquired multi-source heterogeneous data through a protocol analysis engine module to obtain structured data; the intelligent data screening and deconstruction module is used for analyzing, associating and identifying the structured data to obtain a key business entity; the lightweight packaging module is used for carrying out serialization and compression optimization on the key business entities to obtain lightweight packaging data; and the rendering module is used for carrying out real-time rendering on the basis of the lightweight packaging data to obtain a real-time visual operation state of the electric power communication SDH network. According to the invention, the acquisition efficiency is optimized, real-time monitoring is supported, the bandwidth cost is reduced, invalid data transmission is reduced by 80%, and the fault positioning time is compressed from an hour level to a minute level.

The invention discloses a point cloud semantic segmentation method and system based on a state space model, and relates to the technical field of computer vision, and the method comprises the following steps: obtaining a point cloud sequence, inputting the point cloud sequence into a pre-established state space model, distributing the point cloud sequence to different pre-established ConvMama blocks through a preset hz serialization mode, and obtaining a semantic segmentation result; performing rearrangement to obtain an ordered point cloud sequence; and carrying out local aggregation and global aggregation on the ordered point cloud sequence based on a pre-established ConvMama block so as to realize point cloud segmentation.

The invention provides a large-scale privacy set intersection method and system based on dynamic bucket load balancing. According to the method, an OPRF environment based on an elliptic curve is used in a logic bucket to replace a traditional RSA scheme, and batch processing of the vector OPRF technology is achieved; oPRF calculation is optimized by adopting orthogonal basis vector projection, so that the calculation complexity is remarkably reduced; generating a global ciphertext intersection through a cross-bucket multi-stage aggregation mechanism; an LRU caching strategy and FlatBuffers serialization are achieved on the optimized transmission data, and the data are compressed in combination with difference value coding; and finally, safely decrypting and generating a formatting result. According to the method, the calculation speed is greatly increased, memory occupation is reduced, meanwhile, high safety is kept, and the method is suitable for large-scale privacy protection data analysis application scenes.