1185 results about "Software system" patented technology

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.

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 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.

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 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 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 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.

The invention discloses an active overload protection method and device for a network-forming energy storage system, equipment and a medium, and relates to the technical field of active overload protection of energy storage systems. The active overload protection method is applied to the BMS, and comprises the following steps: monitoring the output power of the BMS in real time, and determining whether the network energy storage system is in overload operation or not according to the real-time output power of the BMS. And determining the real-time maximum allowable power according to the real-time output power of the BMS in response to overload operation of the networking type energy storage system. And sending the real-time maximum allowable power to the PCS in real time, and controlling the output power of the PCS by the PCS according to the real-time maximum allowable power. Wherein the real-time maximum allowable power is the current maximum allowable power of the networking type energy storage system. According to the invention, the problem of power abuse generated when the network-forming energy storage system is overloaded at different power sections can be avoided, and meanwhile, the complexity of integration of an alternating-current and direct-current side software system is reduced.

The invention provides a software system intelligent development method and system based on an AI technology, and relates to the technical field of software development. Through natural language demand analysis, intelligent component recommendation, code-free generation and continuous optimization closed loop, the problems that a traditional development mode is low in efficiency, insufficient in intelligence and rigid in system are solved, and the method has the advantages that the development threshold is lowered, the development efficiency is improved, continuous optimization of the system is achieved, and data security is guaranteed.

The invention relates to the technical field of data processing, and provides a test case generation method and device, equipment, a storage medium and a program product, and the method comprises the steps: obtaining a Petri net model of a to-be-detected software system; based on a Petri network model, constructing an initialization environment for deep reinforcement learning; performing deep reinforcement learning training in the initialization environment to obtain a decision model for generating a test sequence; and generating a test sequence according to the decision model. By implementing the technical scheme disclosed by the invention, the generation efficiency of the test case can be improved.

The invention relates to the field of power distribution network graph optimization, in particular to a power distribution network planning graph visibility optimization method, which comprises a layered architecture module, a technical module, a data integration module, a data processing module, a data evaluation module, a visualization algorithm module and a user interaction module. The layered architecture module is divided into a presentation layer, a control layer, a business logic layer and a data access layer through a modern software system; the presentation layer is responsible for interacting with a user and providing a user interface; the control layer is used for processing user input, the processing speed and the response time of the system can be effectively improved and the maintenance cost can be effectively reduced by setting the modules to be mutually connected for use and improving the system performance and architecture, and the maintenance cost of the system is reduced and the availability of the system is enhanced through modular design and automatic deployment. The stability and reliability of the system are improved, uninterrupted operation of key services is ensured, technical innovation is promoted, and a theoretical basis and practical guidance are provided for technical innovation in related fields.

Systems and methods are disclosed for detecting code injection vulnerabilities in software systems. In embodiments, an injection string is created to implement an exploit against a software system. The exploit includes an unauthorized executable code that will generate an easily detectable token if executed by the software system. The string is injected into the software system during execution to simulate a code injection attack on the software system. The software system's execution result is obtained and analyzed to determine whether the execution detection token was generated. If so, the software system is proven to be vulnerable to the attack, and the vulnerability may be indicated in an alert or a report. The vulnerability detection technique may be for both software under development as well as production software systems running in the wild to analyze and monitor these systems for code injection vulnerabilities.

The invention provides a method and a system for information interaction between a low-altitude aircraft and an intelligent high-speed roadside unit, and relates to the field of information interaction, and the method comprises the steps: carrying out the data partitioning processing of the flight information of the low-altitude aircraft obtained in advance, carrying out the encryption processing of each data block, and transmitting the encrypted information to the intelligent high-speed roadside unit; determining a real-time flight path of the low-altitude aircraft by combining a multilateral measurement method, and transmitting the real-time flight path to the central control platform; and comparing the real-time flight path with a preset flight path, studying and judging, optimizing the flight path of the low-altitude aircraft by using an improved artificial potential field method according to a studying and judging result, generating a flight optimization instruction, and executing flight adjustment by the low-altitude aircraft according to the flight optimization instruction. According to the invention, the intelligent high-speed roadside unit, which is equipment with unified hardware facilities and software systems with strong edge computing capability, is used as a medium for information interaction of the low-altitude aircraft, and has strong perspectiveness, innovativeness and marketability.

The invention discloses a method and system for reversely generating a design book through codes driven by a large language model, and relates to the field of software engineering. According to the method, code engineering is analyzed through a grammar analysis technology and a big language model, the incidence relation of visible explicit and implicit codes is mined, and a knowledge graph is formed. And function points are mined from a project by applying heuristic search in combination with a vector clustering technology. And relying on the knowledge graph, a summary design book and a detailed design book are reversely generated for code engineering by utilizing a large language model, so that the technical difficulty of maintenance and migration of a software system is reduced in an auxiliary manner.

The invention belongs to the technical field of software systems, and discloses a distributed artificial influence weather operation cooperation instruction issuing system based on the Internet of Things, which adopts a cloud side end three-layer architecture. The cloud platform layer fuses multi-source meteorological data through a perception model, the rain cloud tracking prediction model analyzes rain cloud changes, and the task model generates an operation instruction based on a large language model; the edge node layer processes and distributes a cache instruction, and supports local decision when no cloud instruction exists; and the terminal equipment layer completes data collection and job execution. According to the method, multi-source data space-time fusion, rain cloud trajectory dynamic tracking and intelligent generation of the operation scheme are realized, the optimal catalysis window can be determined, the equipment type, position and operation parameters can be accurately planned, and dynamic adjustment of redundant equipment is supported. The operation accuracy and efficiency are remarkably improved in effect, the operation opportunity judgment error is reduced, the equipment response delay is shortened to the second level, the rain increasing efficiency is improved, and reliable support is provided for disaster prevention, activity guarantee and the like.

The invention relates to the technical field of testing, can be applied to the fields of financial science and technology and medical health, and discloses a software testing method, device, equipment and medium based on heterogeneous multi-agent, the method comprises the following steps: receiving a to-be-tested task, and analyzing the to-be-tested task to generate a task vector; for each heterogeneous agent in an agent pool, constructing a capability vector of the heterogeneous agent, and determining a capability matrix according to the capability vector and the task vector; generating a test workflow of the directed acyclic graph according to the task vector and the capability matrix; aiming at each task node in the test workflow, performing graph enhancement retrieval from a domain knowledge base to obtain a retrieval result, and injecting the retrieval result into a control instruction of the task node as enhancement knowledge; and calling a preset test tool to execute a test case generated by the test workflow. And the test quality and the test efficiency of the complex software system are improved.

The invention relates to a product customization and configuration method and system based on a product model and a knowledge base. The method comprises the following steps: (a) associating a product structure model and a product knowledge base of a target product series; (b) constructing a product configuration model integrating a product structure model, a product knowledge graph and a configuration rule; (c) customer demand parameters are received, and a customized configuration scheme is generated through the product configuration model; (d) automatically generating a design material list and a process material list based on the customized configuration scheme; and (e) executing cost accounting according to the generated design bill of material and the process bill of material and outputting quotation data. The method has the advantages that the requirement of a user for customization can be met, the requirement for configuration can also be met, data such as technologies and processes can be automatically configured for the user, the data can be synchronously updated to related software systems such as PLM and PDM, and the intelligent design and intelligent manufacturing capacity of an enterprise is greatly improved.