1523 results about "Computer compatibility" patented technology

The invention discloses an AI model automatic deployment platform based on a containerization technology, which relates to the technical field of automatic deployment of an artificial intelligence model, and comprises a container construction and intelligent configuration module, a transmission and cache management module, a security and multi-version warehouse module, a resource scheduling and optimization module and a deployment and interface management module, the container construction and intelligent configuration module adopts a three-layer mirror image construction strategy of a base layer, a framework layer and a model layer. According to the invention, all the modules cooperate to form a closed loop, after the container construction module generates an incremental packet and the incremental packet is subjected to security signature verification, the transmission module distributes the incremental packet according to network quality, the resource scheduling module dynamically adjusts bandwidth and quota, and the deployment module starts the container and monitors the container in real time. The problems of efficiency, safety and resource optimization of model deployment in an edge environment are solved, and cross-platform compatibility and service continuity are improved.

The invention relates to the technical field of data processing, in particular to an intelligent document updating processing method and system.The intelligent document updating processing method comprises the steps that part parameters and coding rules are extracted from engineering design file metadata, a part list and a PDM and PLM system, the semantic association relation between parts is analyzed through a knowledge graph, and the mapping relation between logic identifiers and physical files is constructed; generating an initial version file library; and monitoring file names and version changes in real time based on a micro-service architecture, calling a simulation service in combination with a knowledge graph to verify parameter compatibility, screening an optimal version, updating the optimal version to a main version library, optimizing historical conflict data in a block chain evidence storage index by using a genetic algorithm to generate a standardized code, and outputting a cross-platform parameter mapping table. According to the method, the problems of file name change, non-standard coding conflict, version control missing and cross-platform adaptation incoherence are solved, and the data tracing efficiency, the version management reliability and the system compatibility are improved.

The invention provides an AI-based laboratory equipment scheduling optimization method and system, and the method comprises the steps: firstly obtaining a state monitoring data set containing the characteristics of equipment operation power consumption, idle time length, environment interference factors and the like in real time, and then carrying out the multi-dimensional analysis of the state monitoring data set; generating an availability evaluation index set containing characteristics of equipment load fluctuation, maintenance period prediction, compatibility matching and the like, and an experiment task priority queue, performing cross decision analysis on the availability evaluation index set and the experiment task priority queue based on a preset dynamic resource allocation model, and obtaining a scheduling strategy set containing a task allocation path, a cooperative operation rule and a conflict resolution mechanism; scheduling strategy parameters are calibrated according to experimental task operation log data, an optimized execution instruction set is generated, instructions are fed back to an equipment control system to adjust the equipment state, a dynamic resource allocation model is iteratively updated according to execution feedback data, and efficient scheduling optimization of laboratory equipment is achieved.

Systems and methods for generating industry-specific solutions using collaborative Artificial Intelligence (AI) agents are disclosed. In an aspect, input data corresponding to an industry-specific problem is received. A goal context for the industry-specific problem is then identified. Further, an industry-specific process workflow corresponding to the industry-specific problem is selected based on the goal context. Furthermore, an agentic context, historical intelligence data, group dynamics data for agent compatibility, appropriate agent character data, and historical user feedback data corresponding to the industry-specific workflow are retrieved. Moreover, AI agents and agent compatibility rules to execute user goals are selected and the rules are assigned to each AI agent. An agentic process workflow for the industry-specific problem is then generated. A candidate solution is then generated by executing the generated agentic process workflow. The candidate solution, agentic process workflow and agent compatibility rules are then outputted on a user interface of a user device.

The invention provides a Linux access control system based on attributes, and relates to the technical field of data access control. The system comprises a system monitor module, a data interaction module and a decision unit. The system monitor module collects attributes from a kernel and a user space and writes the attributes into the data interaction module; the access decision unit compiles the access control strategy into an eBPF program and mounts the eBPF program to a corresponding hook; executing the kernel to the hook, and triggering an eBPF program; an eBPF program queries a Flow rule; matching the attribute with the Flow rule, and if the matching is successful, executing a corresponding action; if all the Flow rules fail to match, executing a default action; the system can be expanded during operation, and can be loaded or unloaded based on dynamic loading characteristics and strategies of the eBPF program and the eBPF program during operation of the system, so that the problem that a kernel needs to be compiled in a traditional LSM scheme is solved; the method does not intrude the kernel, is completely based on an eBPF program, does not modify a kernel source code, and can guarantee the stability and compatibility.

The invention provides a cross-protocol identification data intelligent analysis middleware method and system. The method comprises the following steps: taking multiple industrial communication protocol identifiers in advance to form a metadata set, and constructing a protocol feature element model library containing a non-standard protocol feature template; a programmable logic device is introduced, physical layer signal waveform characteristics are matched, and corresponding communication protocol stacks are dynamically switched at a hardware level according to a matching result; receiving a communication data stream through an electric domain processing unit of the programmable logic device, and generating a protocol analysis logic sequence in combination with a state transition rule of the meta-model library and a mode matching rule of the protocol stack; performing combined operation on the structured feature template and the analysis logic sequence to generate an executable cross-protocol data analysis rule; and when unknown protocol characteristics are detected, triggering dynamic reconstruction of the template, so that the analysis rule is adapted to a newly added protocol. According to the technical scheme provided by the invention, the protocol compatibility development cost in an industrial multi-protocol scene is remarkably reduced.

The invention discloses a heterogeneous equipment intelligent management system and method based on an artificial intelligence technology, and relates to the technical field of computers, and the system comprises a heterogeneous equipment intelligent management platform: a heterogeneous equipment layer is provided with multiple types of equipment; the equipment access layer supports a plurality of communication protocols; the device adaptation layer is used for converting communication protocols and data formats of different devices into model context protocols, and abstracting the devices by using a heterogeneous device unified description language; the resource management layer is responsible for monitoring the resource use condition of each device in real time and carrying out dynamic resource allocation according to a preset strategy; the task scheduling layer is used for intelligently distributing tasks according to task requirements and equipment capabilities and realizing cooperative work among equipment; and the user interface layer is used for providing a user interface, so that a user can conveniently configure, monitor and manage the equipment. According to the invention, heterogeneous equipment can be effectively managed, the compatibility and efficiency of equipment management are improved, the management cost is reduced, and intelligent control is realized.

The invention discloses an AI-based localized environment application offline deployment method and device and a medium. The method comprises the steps that a localized environment is pre-detected based on probe service, a system layer configuration list is generated, heterogeneous features are extracted through a dual-channel deep learning model based on the system layer configuration list, and adaptive priority labels of the heterogeneous features are generated; the method comprises the following steps: analyzing an application program through static analysis and dynamic scanning technologies, constructing a dependency list, and constructing a version compatibility atlas based on metadata of a domestic operating system warehouse; generating a minimum-cost replacement path by using a graph attention network, performing hash check on each dependency packet, and generating an incremental update packet through a differential packaging algorithm; the resource occupation condition of the system is monitored in real time through a reinforcement learning model so as to dynamically optimize a deployment sequence and a resource allocation strategy, a deployment log is analyzed based on a natural language processing model, an error type is recognized, a repair operation is triggered, and application offline deployment is achieved.

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 data processing system based on an artificial intelligence algorithm, and relates to the technical field of artificial intelligence and data processing, and the system comprises a data input interface which is used for receiving a multi-source heterogeneous data stream; and the preprocessing engine is connected with the data input interface and comprises a dynamic metadata sensing unit, an incremental quality evaluation unit, a bidirectional verification self-repairing unit and a closed-loop feedback optimization unit. According to the data processing system based on the artificial intelligence algorithm, through dynamic metadata perception and an incremental quality evaluation mechanism, the manual intervention requirement of a multi-source heterogeneous data preprocessing stage is reduced, and the problem of adaptation stiffness caused by the fact that a traditional method depends on a static rule is solved; by combining the bidirectional verification design of the business rule and the model feedback, the dual reliability of the data recovery strategy in logic rationality and algorithm compatibility is ensured, the risk of overfitting or scene mismatching caused by a single verification mechanism is avoided, and the input data quality and decision accuracy of a downstream artificial intelligence model are improved.

The invention relates to a power system digital twin model generation method and device, computer equipment, a readable storage medium and a program product. The method comprises the steps of obtaining multi-source heterogeneous data of a transformer substation, preprocessing the multi-source heterogeneous data to obtain preprocessed data, performing fuzzification processing based on the preprocessed data, extracting a fuzzy logic rule, constructing a to-be-trained adversarial network model, and training the to-be-trained adversarial network model based on the fuzzy logic rule. And when it is determined that the model verification conditions are met, training is stopped, and the digital twin model of the power system is obtained. In the modeling process, a model combining fuzzy logic and a generative adversarial network is introduced, and through dynamic rule base management and self-adaptive updating, it is ensured that the system can adapt to the complex operation environment of a power system in real time, so that the accuracy of automatic generation of the substation operation order is greatly improved, and the automation degree of the substation operation order is improved. And meanwhile, the rule compliance and the compatibility of the power system standard are ensured.

The invention provides a power domain control software architecture and a controller thereof, the software architecture comprises an application layer developed based on an AUTOSAR standard, a runtime environment and a basic software layer, the application layer comprises a plurality of modularized software components, the software components package service logic functions of the controller to be integrated, the software components are provided with an AUTOSAR-based ARXML file definition interface, and the AUTOSAR-based ARXML file definition interface is used for defining the service logic functions of the controller to be integrated. According to the method, the interface compatibility can be improved, seamless integration of application layers across controllers can be realized, and the development parallelism is enhanced by kernel deployment of independently packaged software components according to the real-time performance of functions. The runtime environment generates an inter-core communication agent code, constructs a virtual bus and routes a service request of an application layer component to the basic software layer, and the basic software layer provides system-level service for the application layer through a virtual bus interface of the runtime environment, so that the application layer does not need to directly operate hardware, software and hardware decoupling is realized, and the service performance of the application layer is improved. And smooth realization of software fusion and integration is promoted.

The invention provides a credential and credential environment adaptation method and system in combination with a cloud computing platform, and relates to the technical field of credential and credential environment adaptation, and the method comprises the steps: generating adaptation analysis basic features based on static structure information; performing interface adaptation analysis by using the API support database and the adaptation analysis basic features; after a virtual credential environment is established by utilizing the virtual machine, executing the operation record of the target software, and establishing an operation data set; behavior capture of the operation data set is carried out under the guidance of the risk hot area of the compatibility matching graph by utilizing a behavior abnormity identification channel, and clustering analysis is carried out on behavior capture results; uI presentation of the target software is recorded, presentation consistency verification is carried out based on the UI presentation, and a presentation consistency score is established; and establishing an adaptation result according to the adaptation risk score table, the behavior stability score and the presentation consistency score. According to the invention, the technical problem of poor adaptation stability in the prior art can be solved, and the technical effect of improving the adaptation stability is achieved.

The invention belongs to the technical field of computers, and discloses a computing power dynamic segmentation method and system, computer equipment and a storage medium, and the method comprises the steps: collecting system historical load data and a task execution mode, and predicting a future resource use condition by adopting a machine learning algorithm model; monitoring the overall load data of the system; searching available computing resources in the system; if the overall load data of the system exceeds an overall load threshold value, the resource demand of a single task exceeds a resource tension threshold value and / or the future resource use condition exceeds the computing power of the system, triggering a computing power segmentation decision; matching the compatibility between the available computing resources and resource parameters of a to-be-executed task, and obtaining a compatibility result; according to the compatibility result, the available computing resources and resource parameters of the to-be-executed task, a segmentation strategy is formulated; and redistributing the available computing resources according to the segmentation strategy. The technical problems of low resource utilization rate and long waiting time existing in computing power segmentation can be solved.

The invention provides a notebook computer pre-installed environment simulation test method and system based on a virtualization technology, and the method comprises the steps: firstly creating a virtualization environment instance which is matched with a target notebook computer hardware architecture and comprises a pre-installed operation system and a preset software set, and then injecting a simulation user operation behavior sequence according to a predefined environment configuration strategy, the method comprises the steps of generating a dynamic test case set, executing and monitoring running state data in a virtual environment instance, generating an environment compatibility analysis report by extracting an abnormal response mode in the running state data, and finally adjusting resource configuration parameters of the virtual environment instance according to the environment compatibility analysis report. And generating the optimized pre-installed environment mirror image file, so that efficient and comprehensive simulation test on the notebook computer pre-installed environment is realized, and the test efficiency and accuracy are effectively improved.

The invention relates to a low-code visualization optimization method and system for power credential development, and the method comprises the following steps: S1, obtaining data in a credential environment, recognizing the interface difference and performance bottleneck points between components through a compatibility test matrix, and building a standardized adaptive interface specification; s2, obtaining a standardized business object model based on the standardized adaptive interface specification; based on the business object model, defining attributes, relationships and constraint rules of each business entity, and forming a data dictionary and an interface specification special for the electric power field; s3, on the basis of the data dictionary and the interface specification special for the power field, constructing a DSL (field specific language) of power visualization; s4, a dragging type interactive design is adopted, and the visual operation is converted into a DSL configuration file; and S5, automatically converting the DSL configuration into a React or Vue component code through a code generation engine, and carrying out compatibility optimization on the kernel of the creative browser. According to the invention, the power credential development efficiency is effectively improved.

The invention discloses a multi-dimensional evaluation method for credential environment migration, and particularly relates to the field of evaluation. According to the method, an evaluation index system covering infrastructure, an application system, data resources and security compliance is constructed, and comprehensive scanning is realized through instruction set detection, binary analysis and equal security compliance inspection; dynamically calculating the weight by adopting an AHP-entropy weight hybrid model, and triggering weight rebalance based on the volatility; through combination of static scanning and a dynamic probe, the compatibility, performance and safety differences are quantitatively analyzed; and finally, generating a visual risk assessment report, integrating data of each stage to calculate a compatibility score, a performance loss rate and a security level, providing instruction level difference comparison, a performance flame graph and a vulnerability attack path interaction view, and outputting a migration suggestion comprising a priority reconstruction list, an NEON instruction optimization scheme and a staged reinforcement plan.

The invention discloses an AI-based building design scene automatic intelligent generation method and system, and relates to the technical field of building design, accurate basic data is provided for subsequent design by constructing a building related data set H, then a three-dimensional space model of an ancient building is generated by using 3D modeling and image recognition technologies, and the three-dimensional space model of the ancient building is obtained. According to the method, the to-be-repaired model area is marked, the recognition accuracy of the to-be-repaired model area is improved, in material suitability analysis, the building repair material is selected according to the material suitability score CL, the material compatibility and the repair effect are ensured, a first ancient building repair design scheme is obtained according to the AI model, meanwhile, finite element analysis is conducted, and the recognition accuracy of the to-be-repaired model area is improved. According to the method, it is ensured that the repaired historic building can bear loads and keep stability, finally, through target function optimization and simulation, an optimal historic building repair design scheme is output, a repair plan is executed, high efficiency and accuracy of the historic building repair process are ensured, and the repair quality of the historic building is improved.

The invention provides a heterogeneous computing power resource pooling scheduling platform which comprises a task and resource tensor construction module, a scheduling decision module, a breakpoint state generation module and a rescheduling and recovery module. According to the platform, task requirements and node resources are modeled into a tensor structure in a unified mode, and accurate initial scheduling is achieved through a structured scoring function containing conditional penalty terms. In the task running process, interruption judgment is carried out based on the node dynamic state and the scheduling score, and a breakpoint state tensor containing an execution state is generated. When interruption occurs, a proper node is selected through compatibility screening, and task seamless migration and execution recovery are realized by utilizing the stored state. According to the method, the technical problems of non-uniform resource expression, lack of scene adaptability and incapability of realizing uninterrupted migration in a heterogeneous computing power environment are solved, and the resource utilization rate and the service continuity are remarkably improved.

The invention relates to the technical field of artificial intelligence and software development, in particular to a context-aware code generation and real-time optimization method and system based on a large language model.The method comprises the following steps that a compiler front-end technology is utilized, input codes are analyzed, and an abstract syntax tree is constructed; function definition, variable declaration and statement block structure information are extracted through an abstract syntax tree, so that syntax and semantic structures of codes are obtained, and basic data support is provided for subsequent code generation and optimization; the method has the beneficial effects that the context graph covering grammar, semantics and runtime behaviors is constructed by fusing static grammar analysis (AST construction) and dynamic execution tracking (such as variable value change and function call time sequence), and the problem of context perception deficiency in a traditional code generation technology is solved. And the compatibility, the maintainability and the expandability of the generated code and the project architecture are improved.

The invention relates to the technical field of software and hardware compatibility testing, in particular to a domestic software and hardware compatibility intelligent evaluation system and method, and the system comprises a compatibility analysis engine, a compatibility detection task execution engine, a software behavior analysis module, a fault positioning module, a knowledge graph module, a performance optimization suggestion module and a visual interface. The software behavior analysis module constructs a high-dimensional feature space of a software running state through deep learning, and extracts abnormal features based on a topological space mapping technology; the fault positioning module constructs a dynamic causal network based on a probability graph model, and identifies a root node through a chaos sensitivity analysis algorithm; the knowledge graph module constructs a software and hardware dependency relationship model and provides priori knowledge for fault location; and the performance optimization suggestion module generates an optimization scheme based on the root positioning result, so that the functions of automatically executing a compatibility test, accurately positioning the root of the compatibility problem and providing targeted optimization suggestions are realized, and the adaptation efficiency of domestic software and hardware is greatly improved.

A computing system is disclosed for generating fulfillment-ready coordination sessions based on tokenized item data and user-submitted participation parameters. The system ingests structured, semi-structured, or unstructured item data from merchant sources and applies schema-aligned transformation logic to normalize the data into structured item representations. The normalized records are tokenized into machine-readable item tokens that encode fulfillment constraints and canonical attributes. The system receives user item selections and associated participation parameters, encodes the item tokens and participation data into structured vector embeddings, and applies compatibility scoring logic using vector comparison and rule-based threshold evaluation. Compatibility scores are evaluated against session eligibility constraints derived from the item tokens. When eligibility conditions are met, the system generates a coordination session payload comprising match outcomes, proportional pricing, and fulfillment metadata, and issues orchestration instructions to external fulfillment systems for group-based delivery or preparation execution.

The invention discloses a method and a system for realizing hosted API automatic testing by an API gateway, belongs to the technical field of computer software, and aims at solving the technical problems of how to realize non-intrusive and automatic API testing, ensuring that monitoring and verification can be directly carried out on an API flow level, improving the testing efficiency and the coverage rate and improving the testing efficiency. According to the technical scheme, the method comprises the steps of test case generation, wherein an interface structure is automatically analyzed based on an API document, and a basic test case set is generated; flow capturing: monitoring the actual calling condition of the API in real time, and recording the specific content of each request and the corresponding response data; carrying out flow playback: carrying out playback test based on the captured historical flow, and verifying the compatibility of the API; assertion verification: assertion judgment is performed on indexes of the return value and the response time through an assertion mechanism, and abnormity is found; and test report generation: aggregating test result data, generating a test report containing success rate, response time and error log detailed information, and supporting HTML / PDF / JSON export.

The invention provides an algorithm plug-in collaborative development method and system based on multi-version control, and relates to the technical field of software development, and the method comprises the following steps: creating a development task and distributing the development task to a plurality of development nodes, constructing a dependency graph and analyzing version conflicts, performing code review, and deploying and testing in test environments of different version underlying frameworks. The compatibility score is calculated through the support vector machine, the release version is determined, and the release information is synchronized, so that collaborative development of multiple development nodes is realized, dependency conflicts are recognized in advance, and the compatibility of the algorithm plug-in under a multi-version framework is ensured.

The invention relates to the technical field of artificial intelligence and software development, in particular to a DevOps assembly line intelligent generation system based on natural language description. According to the intelligent generation system for the DevOps assembly line based on the natural language description, an interaction layer is responsible for visually editing an interaction interface and outputting a generated executable script and a flow chart to a user; the intelligent processing layer takes Python and Go as development languages, fuses a knowledge graph technology, supports a dynamic code generation capability and realizes intelligent business process management and automatic execution; the infrastructure layer comprises three core modules, namely a metadatabase, a sandbox environment and an execution engine, and is responsible for providing basic support and resource guarantee for system operation. According to the intelligent generation system for the DevOps assembly line based on the natural language description, the configuration threshold of the DevOps assembly line is remarkably reduced, the cross-platform compatibility is ensured, the safety and reliability of the assembly line are greatly improved, and production accidents caused by manual configuration errors can be effectively avoided.

The invention discloses a method for testing compatibility of creative application software based on big data storage. The method comprises the following steps: acquiring environment and version information to generate fingerprints; similar evidence extraction failure feature clusters are recalled; evaluating a case risk sequence based on the cluster and evidence; a version difference event mapping graph is used for obtaining a seed; spreading the seeds to obtain an influence domain and generating an increment regression set; the duplicate removal execution cases are combined, and receipts and judgment are output; inference of a minimum cost explanation path is hit by the failure case atlas; obtaining a consensus root cause according to the explanation path, and generating complementary measurement and verification; and verifying the gating updating atlas and precipitating new evidence to drive self-updating. According to the method, incremental regression and root cause interpretation updating are realized by utilizing differential propagation and anti-fact verification closed loop of the environment and version fingerprints and the compatible relationship knowledge graph, and the method has the advantages of high efficiency, high coverage and high credibility.

The invention discloses a multi-version upgrading method and device based on distribution, equipment and a medium, and relates to the technical field of distribution. The method comprises the following steps: in response to a version upgrading operation event of a target object, performing metadata marking, multi-modal verification and increment extraction on the target object to obtain version upgrading data and a version upgrading type of the target object; wherein the target object comprises system data, a second-open script and an application source code; acquiring a baseline version of the target object from a multi-mode version database according to a version identifier in the version upgrading data; based on the version upgrading type, determining a target version of the target object according to the version upgrading data and the baseline version; and performing compatibility verification on the baseline version and the target version to obtain a compatibility evaluation report of the target version, and deploying the target version according to the compatibility evaluation report. According to the technical scheme, the reliability and efficiency of system version iteration can be improved.

The invention discloses an encryption method, device and equipment, a storage medium and a computer program product, which are applied to the technical field of data encryption, and comprise the following steps: determining an application scene based on the data volume, the security requirement and the encryption sequence when the encryption equipment encrypts; determining compatibility based on the decryption condition of the decryption device; wherein the compatible performance is a decryption type supported by the decryption device; and constructing a cryptographic algorithm combination set based on the application scene and the compatibility performance, and determining an optimal cryptographic algorithm combination in the cryptographic algorithm combination set by taking the cryptographic algorithm combination set as input and taking the target performance of preset encryption equipment as a target, so as to encrypt the to-be-encrypted data based on the optimal cryptographic algorithm combination. Compared with the current encryption directly based on any cryptographic algorithm, the method provided by the invention can determine the optimal cryptographic algorithm combination by taking the performance target of the encryption equipment as the target based on the application scene and the compatibility, thereby improving the computing performance and the security of the cryptographic algorithm.

The invention discloses a research and development platform language intelligent analysis method based on multi-system fusion, and relates to the technical field of language intelligent analysis. According to the method, various language data in a multi-system fusion research and development platform are efficiently processed, cleaned and coded to generate a standardized data set by customizing a data adaptation interface, deep lexical and syntactic analysis and semantic role labeling are performed on the standardized data set, related features are extracted, a semantic mapping network is constructed, and semantic feature vectors are generated; cross-modal feature fusion is realized, deep meanings of language data are comprehensively captured, rich feature input is provided for intelligent analysis, a constructed intelligent analysis model can perform accurate intelligent analysis and intention recognition on natural language instructions and technical documents, and the intelligent analysis model can be used for performing intelligent analysis and intention recognition on the technical documents by means of compiling environment simulation, debugging information semantic optimization and the like. The model training effect is improved, and the analysis accuracy and the hardware compatibility are improved through hardware behavior simulation, real-time data flow analysis, user feedback and other modes.

The invention relates to the technical field of deep learning, in particular to a PyTorch framework-based compatibility expansion system, which comprises a cross-framework model converter, a heterogeneous hardware abstraction layer, a hybrid computational graph execution engine, an intelligent distributed trainer and a self-adaptive optimizer, the cross-frame model converter converts input model formats of different frames into PyTorch executable formats, the heterogeneous hardware abstraction layer supports rear ends of various hardware and automatically selects an optimal calculation path, and the hybrid calculation graph execution engine fuses dynamic graph flexibility and static subgraph optimization and supports dynamic control function execution flow. An intelligent distributed trainer automatically selects a parallel scheme and is compatible with multi-protocol communication, a self-adaptive optimizer performs dynamic optimization based on hardware characteristics and a PyTorch tensor, a cross-frame model converter supports model conversion of multiple deep learning frames, the cost of migration among different frames by a user is reduced, and the universality of the PyTorch frame is improved.