1505 results about "Software system" patented technology

The invention provides an intelligent vulnerability mining platform construction method and system based on a large model. The intelligent vulnerability mining platform systematically identifies and analyzes potential security risks, collects and identifies code snippets with the security risks, arranges the code snippets into risk code paragraphs, identifies target code paragraphs, determines known vulnerabilities, generates context information, captures key events in actual operation, and records real execution paths and state changes. The method comprises the steps of generating a dynamic execution track, simulating different paths, generating an attack vector path, predicting unknown vulnerabilities and credibility scores of the unknown vulnerabilities, testing prediction results of high-credibility scores, confirming and generating real vulnerability information, recording the information to a vulnerability knowledge base, and generating a security report containing vulnerability description, influence ranges, risk levels and repair suggestions. According to the technical scheme provided by the embodiment of the invention, the detection accuracy and efficiency of the security vulnerabilities in the software system are improved.

The invention provides an industrial agent decision-making method and device, electronic equipment and a storage medium. The method comprises the following steps: loading multi-source industrial data to a knowledge graph, and constructing a domain ontology and an MCP ontology; performing parameter fine tuning on a preset base large language model; receiving an industrial process planning demand input by a user, and performing semantic analysis on the industrial process planning demand; processing the query result by utilizing an MCP context strategy to generate a prompt context; inputting the prompt context and the industrial process planning demand into a fine tuning large language model to generate a first process planning scheme; performing rule verification on the first process planning scheme based on the knowledge graph, and judging whether the first process planning scheme meets a preset rule or not according to a verification result; and converting the final process planning scheme into a control script or an interface calling sequence which can be analyzed by an industrial execution system, and outputting the control script or the interface calling sequence. According to the method, the cross-software system compliance process planning with unified knowledge, automatic generation and rule verification can be realized.

The invention relates to the technical field of software systems, and particularly discloses a vehicle matching and positioning system and method based on comprehensive characteristics of a vehicle and a container, and the system comprises a multi-mode perception and intelligent identification module, a dynamic task matching and scheduling optimization module, an intelligent decision and automatic execution module, and a user interaction and operation module. According to the system, the states of a container and a container truck are sensed in a fusion mode through multiple sensors, cross-modal data feature learning is carried out through a Transform-VIM self-attention mechanism, and high-precision container recognition in a complex environment is achieved; meanwhile, based on multi-dimensional feature matching and a Hungary optimization algorithm, a dynamic task matching mechanism is constructed, and it is ensured that the container trucks and the containers are in accurate butt joint; the system optimizes a scheduling strategy through reinforcement learning, dynamically adjusts an operation process, and is linked with automatic equipment, so that intelligent and efficient port container transportation management is finally realized, the risks of wrong loading, neglected loading and operation delay are effectively reduced, and the overall throughput and operation efficiency of a port are improved.

Techniques for leveraging a large language model (LLM) in software development are described. A description of an error message associated with a software system hosted by the multi-tenant provider network is received. An LLM is prompted with an analysis prompt to generate an analysis of the error message, the analysis prompt including the description of the error message, and the analysis of the error message is received from the LLM. The LLM is prompted with a suggested resolution prompt to generate a suggested resolution to a cause of the error message, the suggested resolution prompt including the description of the error message and the analysis of the error message, and the suggested resolution is received from the LLM. A change to resolve the cause of the error message is sent to an originator of the received description, the change based at least in part on the suggested resolution.

The invention discloses a high-efficiency computer software system resource scheduling method, system and device and a medium, and the method comprises the steps: deploying a hardware counter in an operating system, and monitoring the CPU utilization rate, memory occupation, disk I / O, network bandwidth, the use condition of data aggregation system resources and the resource demand of each process or thread in real time; based on monitoring data, analyzing a current resource supply and demand state by adopting an advanced algorithm, and predicting a future resource demand trend; according to an analysis result, a resource allocation strategy is dynamically adjusted, it is ensured that a high-priority task obtains needed resources in time, and meanwhile fairness of a low-priority task is considered; data feedback after scheduling execution is collected, a scheduling algorithm is continuously optimized, and the scheduling efficiency is improved; the system, the equipment and the medium are used for realizing the high-efficiency computer software system resource scheduling method. The problems that an existing computer system is low in resource utilization rate, poor in key task stability and high in operation and maintenance cost are solved.

A method and system provide the ability to process a construction domain query. A natural language user query is obtained within a construction software system. The user query is pre-processed to validate the query. Text from the query is embed into search vectors for a semantic search. A data source having multiple different sections is obtained. The semantic search is performed within each of the sections and identifies semantically relevant sections. The relevant sections are consolidated into a contextual data prompt that is input into an LLM. The LLM, which is trained based on construction data, generates a response that identifies the relevant sections. The response and an identification of the relevant sections is output.

Techniques for leveraging a large language model (LLM) in software development are described. A description of a software development task is received from a user. Data associated with the software system is obtained from a data source. An LLM is prompted to identify at least one aspect of the task which requires clarification from the user, at least partly by providing the obtained data to the LLM and asking the LLM to identify a question for the user which remains unanswered by the obtained data. The question is presented to the user. An answer to the question is received from the user. The LLM is prompted to respond to propose an implementation of the task at least partly based on the data associated with the software system and the answer received from the user. The proposed implementation is received from the LLM and caused to be displayed to the user.

The invention provides a large-model-assisted program function automatic perception fuzzy test method and system, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring a source code and a description document of a to-be-tested program; analyzing the source code and the description document based on the first large model to obtain all functional parameters supported by the to-be-tested program; identifying a dependency relationship and a constraint condition among the functional parameters based on the source code and the extracted functional parameters; executing a fuzzy test based on the dependency relationship between the function parameters and the constraint condition; and automatically analyzing the test result of the fuzzy test based on the second large model to generate a structured report. According to the method, the coverage rate and effectiveness of fuzzy testing can be optimized, and the quality and safety of a complex software system are comprehensively guaranteed.

The invention relates to the technical field of power electronic equipment testing, and discloses a special intelligent testing device and method for a power UPS, and the device comprises a multi-source power supply system which comprises a three-phase program-controlled variable-frequency power supply, a single-phase program-controlled variable-frequency power supply and a DC program-controlled power supply, the output voltage of the three-phase program-controlled variable-frequency power supply is 0-480V and is adjustable, and the output voltage of the single-phase program-controlled variable-frequency power supply is 0-480V; the output voltage of the single-phase programmable variable-frequency power supply is 0-280V and the frequency of the single-phase programmable variable-frequency power supply is 45-64Hz and adjustable; the output voltage of the direct-current programmable power supply is 50-1000V and adjustable; an electronic load system; a signal acquisition system; an embedded control system; and an upper computer software system. A three-phase program-controlled variable-frequency power supply, a single-phase program-controlled variable-frequency power supply and a direct-current program-controlled power supply are integrated to form a multi-source power supply system, a resistive and rectifying dual-mode electronic load is combined, millisecond-level synchronous switching of a voltage level and a load state is realized through an embedded control system, a closed-loop test capability of multi-dimensional performance of the power UPS is formed, and the test efficiency is improved.

The invention belongs to the technical field of artificial intelligence, and discloses an automatic component arrangement and intelligent generation method based on multi-agent collaboration, which comprises the following steps of: finishing an automatic development task of a software system through interaction and collaboration among agents by adopting a multi-agent collaboration framework. According to the method, the pre-training model and the multi-agent system are utilized to automatically complete component selection, arrangement and intelligent generation in a complex application scene, and the method is suitable for multiple fields of industrial control, production scheduling, intelligent monitoring, medical informatization and the like.

Systems and methods for real-time mapping and visualization generation of system components and inter system communications as well as for the generation of real-time recommendations for architectural recommendations. The systems and methods generate hierarchical workflow mappings of a computational network using event data from software applications lineage logs as well as component and artifact repositories. For example, while event data in software applications lineage logs is conventionally limited to identifying that a given process occurred, the systems and methods use the plurality of events detailed in the software applications lineage logs to create, using an artificial intelligence model, a network mapping of how system components are arranged and interact.

Techniques for displaying a graphical user interface of a software development system are described. An initial description of an initial structure of a software system is obtained, the initial description including an identification of interconnections amongst provider network resources that form the initial structure. A modified description of a modified structure of the software system is obtained, the modified structure based on at least a change to the initial structure of the software system described in the initial description. A difference between the initial description and the modified description is determined. An electronic device is caused to display a graphical user interface including a visual depiction of the initial structure of the software system and an indication of the difference, the visual depiction based at least in part on the initial description.

A method of diagnosing a software system of a vehicle includes receiving data related to the software system of the vehicle, identifying an anomalous event based on a pattern of the received data, and collecting contextual information related to the anomalous event. The method also includes inputting the anomalous event and the contextual information to a machine learning model, determining a root cause of the anomalous event by the machine learning model, and based on determining that the anomalous event corresponds to the malfunction, performing a mitigating action.

The invention discloses a gallery integrated internal production software system based on a modular low-coupling architecture, which is characterized in that a system function is divided into a plurality of modularized modules and the modularized modules are packaged and deployed by a uniform interface, so that the coupling degree between the modules is remarkably reduced, and the problem of global code change caused by module dependence in a traditional single architecture is avoided; direct calling is replaced by an event-driven mechanism, asynchronous communication and decoupling cooperation between modules are realized, and the stability and maintainability of the system during function change or addition are improved; the configuration driving mechanism enables the business process to be dynamically adjusted, different business requirements can be met without modifying a program source code, the plug-in management mechanism provides a standardized third-party extension access channel and supports function loading according to requirements, and the limitation that a traditional system is fixed in function and closed in development is broken through; according to the method, the software can run consistently in Windows, Linux, MacOS and other environments, and the key problems that in the prior art, the coupling degree is high, the expansibility is poor, configuration is not flexible, and the platform adaptation capacity is weak can be effectively solved.

The present invention provides a software design method for intelligent vehicle-mounted terminal based on a microservice architecture, and belongs to the technical field of intelligent networked vehicles. Software system development is realized based on the framework of distributed microservices. In terms of software architecture, the single application integrated by the original intelligent vehicle-mounted terminal is divided into a group of small services, and the services are coordinated and cooperated with each other to provide ultimate value for the application layer and the terminal. Each service runs in its own independent process, and services communicate with each other using a lightweight communication mechanism. Each service is built around a specific business and can be independently deployed to a production environment, a production-like environment, etc. The distributed scenario development method based on the microservice architecture enables different service modules to be freely combined to form application scenarios, which is convenient for independent deployment, distributed management and code decoupling, and the applications can be updated and expanded independently, reducing the coupling of hardware platforms and improving software portability.

The invention relates to the technical field of port operation simulation software systems, and particularly discloses a quay crane operation process simulation system and method for port intelligent driving. The quay crane operation process simulation system comprises a data interaction module, a quay crane motion simulation module, a service simulation module, a simulation control module, an operation backtracking module, an analysis and optimization module and a simulation display module. The method has the beneficial effects that a complex port operation process is completely mapped to a digital space through fine dynamic modeling and coding, a port manager can visually observe and analyze the whole operation process in a virtual environment through full-process digitization, and potential problems can be found and solved in time; based on the constructed 3D simulation environment, natural environment data and random events are combined to carry out work process simulation, meanwhile, simulation data are fed back to a simulation system to carry out closed-loop control, simulation errors are reduced, and the work process simulation authenticity is improved.

The invention discloses a multi-modal fusion complex environment target recognition system, and relates to the technical field of environment target recognition. The hardware system comprises a main control board, an edge calculation module, a visible light camera, an infrared imager, a millimeter wave radar and an environment sensor. The software system comprises a data preprocessing module, a multi-modal fusion module and an AI reasoning module; the multi-modal fusion module obtains environment parameters of illumination intensity, humidity and the number of moving targets collected by an environment sensor. According to the invention, multi-mode dynamic fusion of visible light, infrared and radar is adopted, so that the target detection accuracy under low illumination is greatly improved; the end-side reasoning delay is reduced to 80ms and is greatly improved compared with the cloud scheme delay; through dynamic weight adjustment and incremental learning, the omission ratio of the model in a rain and fog scene is greatly reduced; and the lightweight model is adaptive to low-computing-power edge equipment, so that the hardware cost and the power consumption are greatly reduced.

The invention belongs to the technical field of software development and testing, and discloses an artificial intelligence-based software development automatic test case generation system, which is characterized in that an immune heuristic case self-repairing module is adopted, defects are regarded as antigens, antibody cases capable of being self-updated are generated by using a clone selection algorithm, and a gene rearrangement mechanism is automatically triggered when an interface is changed, so that the test efficiency is improved. The details of the use case are adjusted while the core detection logic is reserved; compared with a traditional method, the mechanism can realize use case dynamic adaptation without manual intervention, the maintenance workload is remarkably reduced, and the method is particularly suitable for a complex software system with frequent iteration; the space-time coupling test scene generation engine fuses dynamic scenes such as interaction and state transition of a module and short-time operation after precise coverage login by using a space-time convolutional network; the cross-dimension holographic use case synthesis module integrates multi-source data such as codes, hardware and user behaviors through tensor decomposition to generate a composite use case; functions and performance of software in a complex scene can be comprehensively verified, and test blind areas are remarkably reduced.

The invention discloses an industrial software test case generation method and device based on a large language model, and the method comprises the steps: obtaining a prompt strategy and a knowledge base which is used for storing background description information generated by an industrial software test case; according to the prompt strategy, guiding the large language model to generate a test case according to the knowledge base, and outputting a corresponding first test case; acquiring a system function description model; optimizing the first test case by utilizing a large language model according to a system function description model to generate a second test case; and obtaining test case deployment information, and performing code conversion processing on the second test case by utilizing the large language model according to the test case deployment information and the system function description model to generate a corresponding test case execution file. According to the method, the test case can be automatically generated, the generation efficiency of the test case is improved, the quality of the test case is improved, the test requirement of an industrial software system is met, and the method can be widely applied to the technical field of artificial intelligence.

The invention relates to the technical field of computer network security, in particular to a computer network information security management method and system, which comprises the following steps: S1, risk assessment, S2, security policy making, S3, security protection measure implementation, S4, management measure implementation, S5, security training and propaganda, S6, emergency response, and S7, accident handling and asset identification: comprehensively carding network assets in an enterprise or an organization, comprise hardware equipment, software systems, network services, data and the like, and the importance and value are determined. According to the computer network information security management method and system, through an information security management method, such as a data encryption technology, the data can be converted into a ciphertext form for storage and transmission, and only an authorized officer with a correct key can decrypt and restore the data into original data; therefore, abnormal user access behaviors and potential security holes can be found in time, a basis is provided for improving security measures, and the situation that a database is stolen or leaked in the data transmission process is effectively prevented.

The invention belongs to the technical field of software system optimization and intelligent operation and maintenance, and discloses a shared platform autonomous upgrading method based on a graph neural network, and the method comprises the following specific steps: S1, component state perception and feature coding; s2, constructing a heterogeneous dynamic evolution diagram; s3, dynamic graph neural network embedding modeling; s4, state prediction and conflict evaluation; s5, constructing an upgrade game diagram and strategy reasoning; s6, executing an upgrading plan and real-time feedback acquisition; and S7, graph structure reconstruction and strategy closed-loop optimization. According to the method, the heterogeneous dynamic evolution diagram is constructed, the time sequence label and the state quantification model are introduced, real-time sensing and standardized modeling can be carried out on the running state of each service component in the sharing platform, and compared with a traditional upgrading method depending on static configuration and a predefined template, the upgrading efficiency is greatly improved. According to the mechanism, inductive expression of multi-dimensional features such as node resource occupation, access frequency and response fluctuation is completed in the stage before upgrading.

The invention discloses a test generation and defect prediction method and system based on a heterogeneous program diagram, and the method comprises the steps: constructing a heterogeneous dynamic program dependency diagram: extracting an abstract syntax tree, a control flow diagram and a program dependency diagram of a source code through static analysis, capturing an execution track during operation in combination with dynamic instrumentation, and integrating version evolution information, forming a heterogeneous dynamic program dependency graph containing various types of nodes and edges; heterogeneous graph neural network modeling: designing a four-layer eight-header heterogeneous graph neural network model based on the heterogeneous dynamic program dependency graph, and outputting a function level defect probability and a test case sequence by adopting a type awareness attention mechanism and time coding injection; and multi-task joint training and reasoning: adopting an end-to-end training strategy, jointly optimizing defect prediction loss and test generation loss, preferentially screening high-risk functions during reasoning, and generating a coverage test case. According to the method, the defect detection accuracy is remarkably improved, the test coverage rate is increased, and the method is suitable for enterprise-level complex software systems.

The invention discloses a micro-service-based enterprise software system construction method and device, and relates to the technical field of enterprise software system construction, and the method comprises the steps: carrying out the micro-service division of an initial software system according to enterprise demand information and micro-service boundary information, obtaining an independent micro-service system, and carrying out the evaluation of the independent micro-service system, determining communication architecture information between the independent micro-service systems according to the micro-service configuration information, evaluating the independent micro-service systems to obtain a micro-service configuration influence index, acquiring the micro-service configuration information, evaluating the independent micro-service systems to obtain the micro-service configuration influence index, and determining the communication architecture information between the independent micro-service systems according to the micro-service configuration information. The micro-service architecture is evaluated and optimized through the micro-service configuration influence index and the micro-service independence index, and through quantitative evaluation and the influence index, the micro-service architecture can be judged and adjusted in time, the system design is optimized, and potential performance bottleneck and system excessive dependence are avoided.

The invention relates to a public multi-mode cloud network resource software security enhancement method based on neural symbol fusion reasoning, which comprises the following steps: an intermediate representation generation stage: generating and optimizing an intermediate representation of a program through a fine-tuned large language model, and establishing semantic mapping from a source code to a structured logic representation; in the symbol language conversion stage, the intermediate representation generated by the large language model is converted into a domain-specific language fact set which can be recognized in the symbol logic reasoning stage, and formal and logic expression of program semantics is achieved; and a symbol logic reasoning stage: matching the fact set with the rule base through a symbol logic reasoning engine, performing detection and verification according to the safety rule, generating a structured report, and feeding back a result for optimization. The method is suitable for security enhancement of various core software systems in a public cloud network multi-modal network environment, high-precision security analysis is carried out on cross-modal and cross-subsystem fragmented codes in a compiling-free environment, and verifiable technical support is provided for public cloud network security control.

The invention provides a multi-dimensional operation parameter analysis fault detection method and system, and relates to the technical field of fault detection, and the method comprises the steps: building an inspection task system and a fault triggering system, and forming a task basic data set; generating an inspection task instance object; analyzing to obtain monitoring index data; effective fault report information is extracted, and a software system inspection result set is formed; operating parameters of the hardware system are collected, threshold detection and fluctuation analysis are executed, and a hardware system inspection result set is generated; and extracting multi-dimensional features, inputting a fault scoring function, calculating a comprehensive fault score of each device or system, and automatically generating an inspection report and a fault report sheet. According to the method and the device, the problems that fault evaluation depends on manpower and judgment standards are not uniform in the prior art can be solved, automatic risk grading and result generation based on comprehensive scores are realized, and thus the accuracy and the diagnosis efficiency of fault detection are improved.

The invention discloses a code generation method and device based on a large model, equipment, a medium and a product, and the method comprises the steps: carrying out the code generation through a first code large model according to target code demand data, and obtaining an initial code; according to the first code snippet, generating a test code through a second code large model to obtain a unit test code; according to the unit test code and the initial code, code repair based on editing distance regularization is conducted through a third code large model, and a to-be-analyzed code is obtained; performing iterative optimization on the to-be-analyzed code and the unit test code to obtain a target code corresponding to the target code demand data; according to the method, code generation, unit test generation and code repair are carried out in a multi-model cooperation mode, the reliability and accuracy of code generation can be effectively improved, software security vulnerabilities are reduced, and therefore the stability and security of a software system are improved.

The invention relates to the technical field of all-in-one machine testing, in particular to a testing method, system and device of a GM drive and control all-in-one machine and a medium. The method comprises the following steps: firstly, establishing communication connection between a to-be-tested GM drive and control all-in-one machine and auxiliary test equipment, and ensuring the reliability of data transmission by monitoring a communication state in real time; the test host comprehensively detects the input and output functions of the equipment; then carrying out a servo twin trawling test at a rated rotating speed and under an overload condition, and collecting key operation parameters such as the rotating speed, the torque, the current and the like; meanwhile, an operating system kernel version and a servo software version are verified, so that the compatibility of a software system is ensured; finally, based on all data collected by the test host, a unified qualification judgment standard is established, and only when all detection items meet requirements, the verified factory parameters can be written into the equipment; the problems of missing detection and misjudgment possibly occurring in subentry testing are effectively avoided, and the consistency and reliability of product quality are ensured.

The invention relates to the technical field of hydraulic engineering control, and discloses a local anti-error software system applied to gate pump station remote control, which comprises a remote control center, the remote control center is in wireless communication connection with the local anti-error host through double-channel communication, and the local anti-error host performs double verification on a control instruction sent by the remote control center and a local equipment state through the real-time state double verification unit so as to ensure real-time synchronization of the remote instruction and the local state. The anti-error decision-making unit carries out real-time monitoring and intelligent decision-making on the operation state of the gate pump station through a real-time state monitoring module, a preset water level warning value, an instruction pre-calculation module and a self-locking feedback module so as to cope with dynamic changes such as water level abrupt change, so that the system can adjust the gate opening degree and the operation state of a water pump in time according to the water level abrupt change; the safety and the stability of the gate pump station under complex working conditions are improved, and disasters such as flood caused by the fact that water level changes cannot be handled in time are effectively avoided.

The invention discloses a program fault processing method, device and equipment of a software system and a storage medium, relates to the technical field of computers, can be applied to financial science and technology and medical health business scenarios, and comprises the steps that a fault knowledge graph of the software system is acquired, the fault knowledge graph is dynamically constructed based on multi-source heterogeneous data, and the fault knowledge graph is stored in a database; wherein a diversified atlas relationship is stored; performing graph neural network driven association analysis based on the system component dependency relationship and the first association relationship, mining a hidden fault propagation path on a component dependency link, and determining a fault association component and a fault root cause corresponding to the program fault data based on the hidden fault propagation path; based on the fault association component, the fault root cause and the second association relationship, determining a plurality of candidate repair schemes corresponding to the program fault data; and calculating evaluation scores of the plurality of candidate repair schemes through the utility function, sorting according to the evaluation scores, and determining an optimal repair scheme of the software system about the program fault in the plurality of candidate repair schemes.

The invention relates to the field of robot automation control, in particular to a target board pose tracking device and method based on deep learning, the device comprises a sensor, a target board, an image processor and a displayer, the target board is arranged at the tail end of a mechanical arm, and the sensor is used for collecting image data of the target board and the mechanical arm; the sensor is connected with an image processor through a data line, a software system is integrated in the image processor, the image processor is connected with a displayer, and the displayer displays the posture of the mechanical arm. A software system of the device is based on a deep learning tracking method, an imaging quality deterioration mechanism is considered, imaging quality deterioration data is constructed in a data simulation mode, a simulation data set is adopted to train a pre-training model, then part of data is collected in an artificial simulation environment to serve as real data to be used for training a target detection model, and a target detection result is obtained. And on the basis of continuous frame depth detection features and motion continuity, the accuracy and robustness of target real-time tracking are improved, and finally, an accurate pose result is obtained in combination with standard template data.