684 results about "Open source" patented technology

The invention discloses a video editing method and related equipment, and the method comprises the steps: receiving an editing intention of a user, carrying out the semantic analysis of an intention analysis model, and generating an editing copywriting, a style and a structured instruction; the edited copywriting is split according to the edited copywriting; performing cross-modal analysis on video materials by using a video understanding model trained based on an open-source multi-modal model, and accurately positioning sub-lens segments; secondly, editing the sub-lens segments through an editing model to generate a preliminary sheet, performing quantitative scoring through a video scoring model, and obtaining a scoring result according to a multi-dimensional index; and finally, the result is fed back to the editing model, and iterative adjustment is carried out until a final slice is produced. According to the method, the editing efficiency is improved, the manual operation time is shortened, the material adaptability is enhanced, the personalized requirement is met, the editing effect consistency is improved, the video content understanding is deepened, the editing effect is optimized, the technical threshold is reduced, the existing technical problems are solved comprehensively, and an efficient, intelligent and personalized editing scheme is provided.

The invention relates to the technical field of natural language processing, in particular to a knowledge graph fused reservoir emergency plan large language model review method. The method comprises the following steps: acquiring a to-be-examined reservoir emergency plan, and performing data structured processing; a first neural network model is combined with a multi-scale context sensing mechanism to comprehensively capture local and global semantic information, and knowledge graph representation of a reservoir emergency plan text is constructed; a second neural network model is adopted to process the chart type information, and a reservoir emergency plan multi-modal knowledge graph is constructed; and meanwhile, an open source large language model of a Transform architecture is finely adjusted by adopting comparative learning, a special reservoir emergency plan large language model is constructed, and complementary enhancement of language model semantic embedding and knowledge graph structure representation is realized through a bidirectional enhancement mechanism. And finally, in combination with a graph attention mechanism, embedding the multi-modal knowledge graph into the fine-tuned large language model for comprehensive review, and generating a review report.

The invention discloses a network security situation analysis method and device based on multi-agent collaboration, and the method comprises the steps: firstly constructing an agent architecture and a route for network security situation assessment analysis based on an open source large language model, carrying out the role definition of each agent in the agent architecture through combining with a Prompt prompt word, and stipulating the communication format of the agent; based on domain knowledge, a dynamic rank learning LoRA algorithm is adopted to carry out fine adjustment on each agent in the agent architecture, based on a natural language rule base, a commander agent is utilized to coordinate a collusion agent and a simulated blue army agent, multi-dimensional analysis is carried out through reflection, inquiry and debate mechanisms, and a network security situation assessment result and trend prediction are generated. Through combination of dynamic LoRA fine tuning and a multi-agent debate mechanism, the problems that universality and field adaptability are difficult to balance and complex attack intention analysis is insufficient in a traditional method are solved, and the accuracy and prediction reliability of network security situation assessment are remarkably improved.

The invention relates to the technical field of Internet of Things, in particular to a water meter data acquisition method based on an open source gap, which comprises the following steps of: based on an open source gap distributed architecture, acquiring water meter sensing and communication data, analyzing flow fluctuation, marking low-efficiency nodes, optimizing communication and energy consumption matching, screening abnormal links, and identifying over-limit nodes. And generating an exception and switching control table. According to the method, intelligent division of a data fluctuation interval is realized by analyzing a flow change trend in unit time, on the basis, a data acquisition task and an energy consumption state are matched in combination with an operation period and electric quantity distribution, meanwhile, signal strength and delay in a transmission path are comprehensively evaluated, and the flow output change is continuously tracked, so that the flow output change is accurately determined. And abnormal operation nodes are identified and switching control signals are triggered, so that the stability and accuracy of data acquisition in a complex environment are improved, the coordination and execution efficiency of the whole process are enhanced, the information loss risk caused by communication faults is reduced, and the intelligent response level is improved.

The invention discloses an open source software risk detection method and device based on an SBOM list, equipment and a medium, and relates to the technical field of software detection. The method comprises the following steps: acquiring an SBOM of target software, and analyzing component information of the SBOM; generating a feature character string according to the component information, and calculating a feature hash value through a hash algorithm; based on a known open source component, analyzing component information and vulnerability information of the component, calculating a feature hash value of the component, and storing the feature hash value to form a feature database; searching a matching item in a feature database based on the feature hash value of the target software component, and if the matching item is found, obtaining a license and vulnerability information of the target software component; if not, marking, manually judging and supplementing the data to the feature database; constructing a license compatibility matrix, and checking the compatibility of the obtained license and the overall license of the project; and comparing the acquired vulnerability information with a feature database, calculating vulnerability severity by combining a CVSS scoring model, and generating a priority repair report. According to the method, the software security detection speed and accuracy are improved.

The invention discloses a code fingerprint-based open source component identification and vulnerability detection method, which comprises the following steps of: receiving a local or remote code, extracting a difference file, generating an AST and constructing a code attribute graph; sHA-256 Hash fingerprints and GNN semantic fingerprints are calculated for the function level sub-graphs to form mixed fingerprints, accurate matching is conducted through a Bloom filter, semantic matching is completed through nearest neighbor, and a component version is determined through a distribution difference algorithm. The method comprises the steps that firstly, a component identifier is mapped into a PURL or an SWID, an OSV / NVD library is inquired to obtain a CVE, comprehensive risks are calculated in combination with CVSS, EPSS and dependency depth, and an SBOM and a vulnerability report conforming to CycloneDX or SPDX are output. The method is high in speed and high in accuracy, and the open source risk can be automatically treated in continuous integration.

The invention relates to the technical field of anti-counterfeiting watermarks, in particular to a digital intelligent water affair form text anti-counterfeiting watermark generation method based on an open source gap, which comprises the following steps of: acquiring field difference and repetition frequency, screening jitter fields to generate a perturbation table, indexing and marking sensitive segments in sections, establishing a weight structure and adjusting bit width, and sequencing nodes to generate a binding list. And constructing a watermark path chain, and matching track consistency to generate a drift label. According to the method, a field disturbance weight structure is established through proportional mapping, dynamic affinity sorting is performed in combination with node processing capacity and field task attributes, watermark track sequence binding is realized in a field path structure, and a track consistency detection mechanism is introduced in time sequence matching of field filling and node execution. The concealment of field embedding disturbance and the verifiability of a watermark writing path can be enhanced under the condition of not changing a form external display structure, and it is ensured that node selection in the field content embedding process has logical continuity and a track chain can be traced back.

The invention belongs to the technical field of cooperative control, and particularly relates to a development experiment box multi-device cooperative control method based on an open-source gap system. According to the method, access, identity authentication, automatic discovery and dynamic connection of experimental box equipment are realized through a distributed equipment management platform of an open-source Gao Mongolia system; a uniform resource scheduling mechanism is utilized to complete resource information collection, strategy matching and dynamic allocation; task allocation is carried out by adopting an intelligent task scheduling algorithm and combining equipment states and task requirements; real-time data transmission between devices is realized by means of a low-delay communication protocol; and finally, automatically summarizing and analyzing experimental data and generating a report. According to the method, the problems of low equipment identification efficiency, low resource utilization rate, low task allocation efficiency and the like in the existing development experiment box are solved, the intelligent level and the system stability of equipment cooperative control are improved, and the method is suitable for a multi-equipment cooperative control scene of the development experiment box.

The invention discloses a coupling iterative algorithm library and a reactor multi-physical field coupling calculation program construction method. The coupling iterative algorithm library comprises an open source library layer used for integrating a plurality of numerical calculation libraries and providing an input / output interface for a reactor multi-physics field coupling framework and each numerical calculation library; the core algorithm layer is used for integrating a plurality of coupling iteration algorithms and a plurality of acceleration algorithms related to each coupling iteration algorithm; the interface layer is used for providing a data interaction interface between a nuclear reactor multi-physics field coupling calculation program and the core algorithm layer; and the iterative algorithm template layer is used for forming an iterative solution process control template function based on the open source library layer, the core algorithm layer and the interface layer, and the iterative solution process control template function is used for constructing a nuclear reactor multi-physics field coupling calculation program based on the process control parameters set in the input file. According to the coupling iterative algorithm library, the development efficiency and the development quality of a nuclear reactor multi-physics field coupling calculation program can be improved.

The invention relates to the field of character recognition systems, and discloses a Chinese ancient book character recognition method based on an image recognition technology, which comprises the following steps: reading an ancient book image according to an open source code computer vision library to obtain an original image; performing gray processing on the original image to obtain an image after gray equalization; according to a median filtering algorithm, carrying out de-noising processing on the image after gray scale equalization to obtain a pre-processed image; according to the method, multi-level feature extraction is carried out on the image through the real-time multi-scale detection model and the convolutional neural network, and character features of different scales and angles in the image can be captured; and therefore, the character recognition accuracy is improved, especially for the common font, typesetting, inclination or blurring conditions in ancient book images.

The invention discloses a Linux container-based Android and open source gap dual-system collaborative operation method, relates to the technical field of operation system collaboration, and aims to reduce resource occupation and improve collaboration and switching coherence, and the method comprises the following steps: step 1, by taking an Android as a host, creating a container isolation running domain, starting a namespace and managing resources by a control group; the method comprises the following steps: presetting a binder virtualization and three-state template; 2, deploying an open source gap in a container, mounting a shared directory in a directory binding manner, configuring bridging or address translation, and establishing copy-on-write and snapshot identifiers; and step 3, establishing a cross-system binder channel, and setting a service registration agent in a service manager and completing mapping of a handle and an authority domain by combining a shared memory and a local socket. And step 4, executing system switching without restarting, enabling one foreground and the other background through a foreground interface and continuous quota assignment, freezing a background session, limiting the speed of background heartbeat, and returning according to a snapshot when an exception occurs.

The invention provides a shield tunneling parameter intelligent optimization method and system based on an AI big language model, and relates to the technical field of tunnel construction optimization. Comprising the following steps: training an open source large model to obtain a preliminary shield tunnel construction industry large model; an upstream task and a downstream task which are relatively independent are set, and the upstream task comprises a tunneling speed prediction module, a tool wear prediction module, a ground surface settlement prediction module and a tunneling attitude prediction module; a downstream task combines an RAG technology and an NSGA-III multi-objective optimization framework, four prediction modules and an energy consumption calculation formula serve as objective functions, the value range of constraint conditions is determined, a tunneling parameter optimization module is established, a professional shield tunnel construction industry large model is formed, and scientific and intelligent decision making of tunneling parameters is achieved. According to the method, information in multiple aspects can be comprehensively considered during decision making, the reasoning process is output, and scientificity, comprehensiveness and reliability of decision making are ensured.

The invention discloses a hybrid nonlinear floating fan hydrodynamic load coupling calculation method. The method comprises the steps of obtaining a velocity potential of a wave field in a computational domain through OceanWave3D numerical simulation (a wave field calculation model), and performing post-processing to obtain wave field data; dividing an instantaneous wet surface according to the six-degree-of-freedom motion, calculated based on an OpenFAST (floating fan calculation model), of the floating fan in each time step, and performing pressure integration to obtain a Front-Krylov force; updating the hydrostatic stiffness matrix according to the real-time position change of the buoyancy center of the fan, and calculating hydrostatic restoring force; linear radiation force and diffraction force are calculated through time domain convolution of frequency domain data, nonlinearity of the radiation force and the diffraction force is considered based on an instantaneous wetting volume change coefficient, and hydrodynamic load and structural motion response calculation in the current time step is completed after the OpenFAST is coupled. According to the method, coupling calculation between the open source code OpenFAST and OceanWave3D is achieved, and compared with commercial software at the present stage, the method is more accurate in wave load calculation under the extreme wave sea condition.

The invention relates to an open source component multi-modal dependency risk tracing method and device, and the method comprises the steps: employing a mode of combining static analysis and dynamic analysis to analyze a component dependency relationship of software, and combining AST analysis and construction script analysis; function-level features are extracted, a binary fingerprint database is constructed, the similarity between different versions is analyzed through LSH, behavior patterns in binary codes are analyzed, and hidden dependencies or malicious code injection is detected; the version updating history of the dependent component is analyzed and monitored by using a time sequence, and the vulnerability security of the component is evaluated in combination with attack graph analysis; high-risk components on the path are calculated, potential supply chain attack points are identified, and risk points are subjected to cross validation; a time sequence diagram database is used for recording the component dependency relationship, and time backtracking query is supported. According to the method, a multi-mode dependency analysis method is adopted, potential dependency risks are rapidly identified, and potential supply chain attack risks are timely warned.

The invention relates to the technical field of network security, and discloses a security vulnerability AI positioning method and system based on log files. The method comprises the following steps: firstly, analyzing user feedback describing open source component security vulnerabilities to extract fault key entity information, screening related log entries based on the fault key entity information, and constructing a vulnerability propagation graph; identifying a vulnerability triggering initial point set by using a graph traversal algorithm and a graph neural network, calling a component vulnerability case library to deduce a vulnerability utilization propagation chain and calculating a matching degree with a known open source component vulnerability utilization chain feature, and obtaining a maximum probability vulnerability utilization propagation chain and an associated vulnerability triggering initial point set; and generating a root cause positioning report, and updating the knowledge base or model training data according to a user verification result. According to the method, the initial vulnerability point and the complete vulnerability propagation chain of the security vulnerability of the open source component can be accurately and efficiently positioned, and the positioning accuracy and timeliness are improved.

The invention relates to the field of intelligent diagnosis of oil and gas field development, in particular to a fracturing wellhead device failure analysis method and system based on a large model and a knowledge graph. The system is helpful for solving the problem that the accuracy and timeliness of failure analysis and fault diagnosis of the fracturing wellhead device in the prior art are limited. The method comprises the steps of building a knowledge graph, setting a knowledge graph dynamic updating mechanism oriented to the fault evolution process, building an open source large model and introducing a closed loop feedback mechanism. According to the method, the large model, the knowledge graph and the RAG technology are applied to fracturing wellhead failure analysis, and the intelligent level of the oil and gas industry is remarkably improved; the problem that a large model is high in generalization but insufficient in specialty is optimized, and the accuracy of answer generation is improved.

The invention relates to the technical field of power grids, in particular to a vertical domain distribution network planning method and system based on a large language model. The method comprises the following steps: collecting and arranging a distribution network structure and operation data, and constructing a'graph 'management system; extracting historical planning schemes to form a typical case library; fusing external open source data and source side data to establish a big database and an access interface; constructing a bilateral prediction model and a planning scheme generation model based on machine learning; carrying out secondary pre-training and practical packaging on the open source large language model; planning parameters are obtained in a guiding mode in a question-answer mode, and a scheme is generated. According to the method, the problems of high labor cost, long planning time, large error, unstable planning quality, poor interactivity, high learning threshold and the like of traditional distribution network planning are solved, the planning efficiency and precision are improved through an intelligent technology, the professional threshold is reduced, a brand new solution is provided for coping with the new situation of high permeability of new energy and electric energy replacement acceleration, and the method is worthy of popularization and application. And the practical value is remarkable.

The invention discloses an open source community developer behavior influence assessment method and system based on a large model, and belongs to the technical field of developer assessment, and the method comprises the steps: obtaining the record and statistical information of developers in a software development project; performing multi-dimensional analysis on the text data in the recorded and statistical information by using a large model, and integrating the scores of all dimensions to obtain a first score of the developer; wherein the dimensions comprise technology, communication, cooperation, continuous contribution and code quality; constructing a knowledge graph according to the record and statistical information, and obtaining a second score of the developer based on the knowledge graph; and obtaining a behavior influence evaluation result of the developer based on the first score and the second score. According to the method, the behavior influence of the developer can be accurately evaluated.

The invention discloses a smart home equipment cooperative control method and system based on edge computing, and relates to the technical field of smart home equipment cooperative control, and the method comprises the steps: employing multiple sensors to collect the temperature, humidity, illumination intensity and user behavior data in a home environment, and obtaining an original data set; preprocessing the original data set by adopting a data processing method to obtain a high-quality data set; constructing a multi-modal collaborative decision prediction model based on the high-quality data set in combination with an open-source large language model LLM, inputting the high-quality data set into the collaborative decision prediction model, and outputting a control strategy result; based on a control strategy result, performing instruction distribution and state synchronization on the smart home equipment by adopting a high-efficiency communication protocol between heterogeneous equipment to obtain an equipment operation instruction set; and based on the equipment operation instruction set, computing and storage resources of the edge nodes of the smart home equipment are distributed through a dynamic resource management and load balancing method, and a resource configuration scheme is obtained.

The invention discloses a GIS development method and system for water conservancy and hydropower digital twinning application, and the method comprises the following steps: building a two-dimensional and three-dimensional GIS general development framework, integrating a two-dimensional and three-dimensional GIS engine, and forming a water conservancy and hydropower two-dimensional and three-dimensional GIS general development platform; based on a water conservancy and hydropower two-dimensional and three-dimensional GIS universal development platform, constructing a water conservancy and hydropower spatio-temporal data base and building a digital twinborn scene; based on a water conservancy and hydropower spatio-temporal data base and a built digital twinborn scene, water conservancy and hydropower related business components and GIS application components are modularly constructed; and integrating a front-end framework, and carrying out visual display and application analysis. According to the method, a water conservancy and hydropower general two-dimensional and three-dimensional development platform is built based on a hydropower two-dimensional multi-engine and a three-dimensional GIS open source engine cesium, and a digital twin space-time base is built on the basis. A mode of'platform + plug-in 'is adopted, business components and water conservancy and hydropower GIS application components are developed in a pluggable component mode, jquery, React and VUE mainstream front-end frameworks are compatible, and visual display and application of water conservancy and hydropower businesses are carried out.

The invention provides a fault diagnosis method for a conveying belt. The fault diagnosis method comprises the steps that vibration data, pressure data and temperature data in the belt running process are collected through a sensor module; carrying out denoising processing on the collected vibration data, pressure data and temperature data, and removing abnormal values; feature parameters are extracted from the data subjected to data preprocessing; the extracted feature parameters are uploaded to a private cloud server through an Internet of Things module, and a lightweight algorithm is adopted to analyze the feature parameters; according to the analysis result of the lightweight algorithm, a preset fault threshold value is matched, and the fault type of the belt is output. According to the method, multi-source data fusion and targeted feature extraction are adopted, the fault false alarm rate is reduced to 10% or below, industrial data leakage is avoided through private cloud deployment, and the deployment cost is reduced by 60% compared with public cloud; based on a sensor and an open source tool, deployment can be completed within 30 days without professional teams, and belt conveyors in different scenes such as mine underground and logistics sorting can be adapted.

The present application belongs to the technical field of software engineering, and specifically relates to a multi-function homologous vulnerability detection method based on function selection and semantic equivalent matching. The method comprises two stages of signature generation and vulnerability detection. The signature generation stage comprises: inputting a vulnerability repair patch, extracting a modification method and a key repair method therein; performing semantic equivalent replacement on the extracted key repair method, and generating signatures for the functions before and after the semantic equivalent replacement respectively; the vulnerability detection stage comprises: inputting a target detection software, pre-processing by using a clone detection software to reduce the search space; extracting functions in the target detection software and performing semantic equivalent replacement thereon, generating signatures for the functions before and after the semantic equivalent replacement respectively, and performing two-stage signature matching, and finally outputting whether there is a suspicious multi-method homologous vulnerability in the target project. The method can help identify multi-method homologous vulnerabilities in open source software, reduce the harm caused by homologous vulnerabilities, and improve the security of open source software.

The invention discloses a method for generating a workpiece model based on point cloud data, and aims to automatically generate a high-precision workpiece model through reverse modeling. The method comprises the following steps: dynamically adjusting a data acquisition amount through a line structure light sensor, and obtaining workpiece surface point cloud data; preprocessing the point cloud, including three-dimensional effective area cutting, uniform downsampling and radius filtering to remove interference data and reduce data volume; carrying out clustering segmentation on the preprocessed data by utilizing a point cloud region growing method, separating a bottom surface part from a non-bottom surface part, extracting contour points of each plane region, calculating normal lines, generating vertexes by fitting contour edges and solving intersection points, and forcibly closing unclosed contours to construct complete geometric features; and finally, generating a workpiece model by combining the point, line and surface information packaged by the open source library. According to the method, the low-error and high-precision model can be directly generated for the model-free non-standard workpiece, limitation of manual modeling or model presetting is avoided, and programming time consumption is remarkably reduced.

The invention provides a terrain element automatic extraction method and system based on deep learning, and relates to the technical field of deep learning. According to the method, a convolutional neural network of an encoder-decoder structure is constructed in an open source deep learning framework, initial training and verification are completed in combination with an image enhancement strategy, and an initial extraction model is formed; reasoning a remote sensing image by using the model, and outputting a topographic element vector result; comparing a reasoning result with reference data through an automatic quality analysis tool, positioning missing extraction, mistaken extraction and boundary deviation regions, and generating an error report; and constructing and supplementing positive and negative samples based on the error region, updating the training set and executing model retraining to form a final model. According to the method, a training-inference-feedback-optimization self-evolution closed-loop system is constructed, and the precision of terrain element extraction, the boundary integrity and the shielding adaptability are remarkably improved.

The invention discloses a lightweight cross-platform PLC (Programmable Logic Controller) multi-language code generation method, which relates to the field of industrial process control, and firstly provides a unified intermediate data model, and on the basis, a module for converting each PLC language into the unified intermediate data model is realized; on the basis of a standardized JSON data structure, a modularized unified intermediate language conversion chain is further supported, an analyzer and a generator communicate through a JSON interface, a cross-platform conversion path is supported, and unified intermediate language conversion from PLC to JSON to XML to ST to C is completed; the method comprises the following steps of: integrating an open source compiler and runtime, constructing an open and pluggable automatic execution framework, compiling a generated C code into a platform executable file by using an adaptive GCC tool chain aiming at different target platforms, and deploying a program generated by compiling to a soft PLC runtime system supporting multiple platforms. According to the method, a JSON intermediate data structure which is light in weight, unrelated to languages and uniform in structure and a matched conversion framework of the JSON intermediate data structure are constructed.

The invention provides a geoscience operator organization and question and answer mode retrieval method and device based on a large language model and a knowledge graph, and relates to the field of intelligent question and answer. The method comprises the steps of obtaining question phrases input by a user in response to question and answer operation of the user; inputting the question phrases into the large language model to output entities corresponding to the question phrases; inputting the question phrase and the entity into a large language model, and generating at least one query statement corresponding to the question phrase by combining the large language model with a vector database; constructing a geoscience operator knowledge graph, and outputting a query result corresponding to the query statement by combining the large language model with the geoscience operator knowledge graph; and inputting the question phrase and the query result into the large language model, and outputting a natural language reply result corresponding to the question phrase. According to the method and the device, the problem of relatively large difficulty and relatively high cognitive burden, namely low use efficiency, existing when a common user uses a traditional open source tool is solved.

The invention relates to the technical field of artificial intelligence, in particular to a man-machine interaction system based on an open source large model. The method comprises the following steps: a user request unit receives multi-round dialogue input of a user based on a context interception algorithm and an intention recognition mechanism, and generates a context prompt word and a target agent identifier; the response processing unit receives the standardized request generated by the platform scheduling unit based on an exponential backoff retry algorithm and a JSON path extraction function, extracts a target field and converts the target field into a structured answer; and the knowledge management and state maintenance unit updates each round of questions and answers to a historical context based on a context closed-loop updating mechanism and a binding relation mapping mechanism, and feeds back the updated context to the user request unit. Based on a context interception algorithm and a semantic embedding classification model, multiple rounds of conversations of a user are converted into semantic cues, target agent identifier mapping and intention affiliation reasoning are automatically completed, and high-precision identification of a request intention and dynamic binding of the agents are achieved.

The invention belongs to the technical field of network security and artificial intelligence, and discloses a graph neural network robustness enhancement method and system for open source software supply chain security, and the method comprises the steps: extracting the characteristics of an open source software supply chain dependency graph; dynamically aggregating information according to node centrality, neighborhood and edge types by adopting an adaptive attention graph convolutional network; through combined adversarial training of gradient penalty and virtual adversarial samples, attacks are simulated to improve model robustness; and constructing a multi-level risk model, and comprehensively evaluating nodes, paths and community risks. The system comprises a data acquisition module, a graph construction module, a robustness enhanced GNN training module, a risk assessment module and an early warning module. According to the method, the robustness and accuracy of the GNN in an open source software supply chain security scene can be remarkably improved, hostile attacks are effectively resisted, and the software supply chain security is guaranteed.

The invention discloses an automatic ransomware collection and analysis method and system based on a large language model in the technical field of network security, and the method comprises the steps: constructing a target data source, collecting a first sample of a ransomware, and capturing the first sample which is actually triggered in an enterprise-level network environment and context behavior data of the first sample from the first sample; an automatic crawler system is established by adopting an open source web crawler framework to establish real-time connection with a malicious code platform, the popularity of a first sample on the malicious code platform is analyzed, and an optimization task is set. According to the method, the natural language processing capacity and the automation technology of a large language model are combined, efficient capture, accurate analysis and quick response of the samples are achieved, an anti-virtualization detection mechanism of the blackmail virus is effectively coped with by implementing an anti-virtualization detection confrontation strategy, and an intelligent protection chain is constructed for network security.

The invention discloses an open source system framework layer custom interface authentication method comprising the following steps: creating a custom service in an open source system framework layer, the custom service providing at least one custom interface; establishing an encrypted and stored authentication white list file, wherein the encrypted and stored authentication white list file comprises a package name and signature fingerprint information of an application needing to be authorized; integrating an authentication trigger mechanism in the custom interface, and executing a hierarchical authentication process when an external application calls the custom interface; the hierarchical authentication process comprises the following steps: acquiring a package name of a calling party application and checking whether the package name exists in an authentication white list file or not; if yes, signature fingerprint information of the calling party application is further obtained and compared with fingerprints recorded in the authentication white list file; and if the fingerprints are consistent, the authentication is successful, and the function logic of the user-defined interface is executed. According to the method provided by the invention, the custom interface is ensured to be only open to the authorized application through a dual verification mechanism of the package name and the signature fingerprint information, and the full choice and stability of the system are ensured.