215 results about "Industrial software" patented technology

The invention belongs to but is not limited to the technical field of software development, and particularly relates to a multi-round reasoning and feedback optimization method and system for industrial software codes, and the method comprises the steps: S1, vector code library establishment: the establishment of a vector code library is composed of three parts of feedback establishment, vector database establishment and format summarization; s2, code reasoning optimization: the code reasoning optimization mainly comprises four aspects: control flow graph analysis, multi-query generation and reciprocal ranking fusion retrieval, reasoning generation subtask and code chain submodule generation, and code combination completion; and S3, feedback optimization: the feedback optimization is composed of score feedback optimization and log feedback optimization. According to the method, an automatic verification mechanism is introduced, logic consistency check and performance evaluation are carried out on the generated and optimized codes, and the correctness and execution efficiency of the codes are ensured. The verification mechanism can find and correct errors in real time in each stage of generation, so that the cost of manual verification is reduced, and the development efficiency is improved.

The invention relates to the fields of computer graphic processing, industrial software technology and the like, discloses a Cocos engine-oriented pipeline loop perception linear decomposition method, and aims to solve the problem that a complex topological graph which cannot be effectively processed and comprises a high-density nested loop in the prior art. The structure is completely and accurately decomposed into a linear drawing instruction sequence which can be accepted by a Cocos engine, and lossless conversion of original topological information is ensured; comprising the steps that after undirected topological graph data are input, a ring detection algorithm is called to recognize and record all independent ring structures in an undirected topological graph and nodes and edges contained in the independent ring structures; judging whether at least one ring is detected in the previous step, and if the at least one ring exists, assigning a determined traversal direction for each detected ring according to a preset ring direction rule; and if the ring does not exist, skipping, and directly performing depth-first search traversal on the undirected topological graph data.

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 provides a data acquisition and monitoring analysis system based on an MES platform, and relates to an industrial software application system. Comprising a low-code development module, a factory modeling module, a monitoring module and a data acquisition module, the low-code development module realizes cross-device linkage development and control program generation through combination of a dragging interface and a device API library in an MES platform integration environment, and guarantees efficient communication control of the device, the factory modeling module integrates two production modes, and the monitoring module is connected with the monitoring module. The monitoring module is used for realizing dynamic modeling and optimizing a production process according to multi-dimensional parameters, the monitoring module is used for realizing ultra-low-delay transmission processing and real-time monitoring of data under high concurrency by applying a dual-cache and message middleware mechanism, and the data acquisition module is used for realizing real-time monitoring of the data based on a machine learning algorithm and a complex event processing technology. Dynamically adjusting the acquisition frequency precision based on the production state; the system aims at improving the efficiency, quality and management level of industrial production, and has wide application prospects and important industrial software application value.

The invention discloses an industrial software cloud base intelligent operation and maintenance and fault self-healing method based on digital twinning, and relates to the technical field of digital twinning. According to the method, the hierarchical digital twinborn model is constructed and dynamically updated, and the operation state of the cloud base is accurately mapped and visually displayed in combination with independent visual modules of a physical layer, a logic layer and an application layer; the method comprises the following steps: calibrating a reference value in real time through a dynamic reference library, quickly identifying abnormity of each layer, generating graded early warning, improving abnormity response timeliness, constructing a dynamically updated fault knowledge graph based on historical fault data, integrating a multi-dimensional correlation analysis and confidence coefficient verification mechanism, positioning a unique fault reason, greatly shortening a fault diagnosis period, and improving fault diagnosis efficiency. And a strategy library is constructed based on a fault knowledge graph and historical self-healing cases, simulation verification is carried out on candidate strategies through a digital twin model, the effectiveness of the self-healing strategies is ensured, and then the self-healing strategies are issued and executed, so that intelligent and automatic fault repair is realized, and stable operation of the cloud base is ensured.

The invention discloses a super-large-scale industrial software supply chain staged anomaly detection method based on a graph neural network, a software anomaly detection task is divided into two stages to be processed, in a rapid pollution stage, a simple model is used for rapidly learning a global anomaly mode, and the anomaly detection efficiency is improved. Possible abnormal nodes and areas are screened out; in the accurate detection stage, a more complex model is used for completing detection of abnormal nodes. Besides, the'confusion degree 'of nodes on the graph is defined according to the Lyapunov coefficient in the chaos theory, and the behavior pattern of abnormal nodes on the graph is rapidly diffused by using the rapid over-smooth'defect' of the graph neural network, so that the propagation process of the whole chaos system is simulated; in this way, the abnormal mode of the global node can be quickly learned, and candidate abnormal nodes and areas can be quickly and accurately screened out.

The invention discloses an industrial software intelligent building system and method based on a plug-in mechanism, and relates to the technical field of industrial Internet of Things, and the method comprises the following steps: defining a standardized format and an interface of a plug-in, and ensuring the compatibility and the expandability of the plug-in; establishing a plug-in library, and analyzing and recording a dependency relationship between plug-ins; performing semantic similarity matching on the user demand and the plug-in function, and screening plug-ins in combination with a plug-in historical success rate to form a plug-in combination; conflict analysis is carried out on the plug-in combinations, CPU occupancy rate detection is carried out on feasible plug-in combinations based on the conflict analysis, and plug-in combinations with unqualified CPU occupancy rates are removed; and activating the plug-ins according to the plug-in combination selected by the user to complete software function implementation, and executing dynamic updating of the plug-ins.

The invention provides a 3D lightweight model threaded hole feature identification method, device and system and a medium, and the method is applied to a cloud server, and comprises the steps: receiving a to-be-identified 3D lightweight model uploaded to the cloud server by a networking device terminal; the 3D lightweight model is recognized, so that a threaded hole in the 3D lightweight model is recognized; reading a threaded hole in the 3D lightweight model to obtain national standard threaded hole information; and feeding back the national standard threaded hole information to the networking equipment terminal, so that the networking equipment terminal displays the national standard threaded hole information and displays a threaded hole rendering graph of the 3D lightweight model. According to the method, the problem that an existing industrial software platform cannot directly recognize the threaded hole characteristics of the 3D lightweight model is solved, meanwhile, national standard threaded hole information and a threaded hole rendering graph can be displayed, the threaded hole characteristic structure can be visually reflected conveniently, and a user can select a bolt assembly conveniently.

The invention belongs to the technical field of business process analysis, and discloses an agent-based industrial software demand acquisition and analysis method and system, and the method comprises the steps: constructing a multi-agent-based demand analysis system; converting user multi-modal input into a structured industrial software demand by using a multi-agent-based demand analysis system; and generating a business flow chart conforming to the BPMN standard according to the requirements of the structured industrial software. According to the invention, the multi-agent system is combined with the demand analysis task, so that efficient and accurate demand analysis is realized, and powerful support is provided for project planning and implementation in the new industrial era.

The invention discloses an industrial software platform system based on a large model, and aims to improve the industrial data processing efficiency and the intelligent level by integrating a large model technology, domain knowledge and an information system interface. The system comprises a vertical large model module, an agent module and a universal API interface module. The vertical large model module adapts a general large model to a specific vertical field by using a GraphRAG technology and a LoRA fine tuning technology. The agent module comprises a memory module, a tool calling module and a planning module, and the autonomous decision-making ability of the large model is improved. The universal API interface module realizes seamless connection between a large model and an industrial information system, and opens up a gap between different information systems. According to the system, efficient data processing and intelligent decision making are achieved through knowledge graph construction, retrieval enhancement generation, model fine adjustment and a universal API interface, and the system adapts to market requirements and industrial environment changes.

The invention discloses a service integrated industrial software cloud base system, which belongs to the technical field of industrial software, and comprises an identity verification and authorization module, which is used for verifying and authorizing the identity of a user, and the authorized user can access an industrial software system; the data access management module is used for managing data access of the industrial software system and safely transmitting the data of the industrial software system; the data monitoring management module is used for managing industrial software system data monitoring and generating an industrial software system analysis report; and the remote maintenance management module is used for performing remote maintenance management on the industrial software system. According to the invention, the problem of poor use effect caused by incapability of accessing, monitoring and managing data of the industrial software system and incapability of maintaining and managing the industrial software system in the prior art is solved. According to the invention, effective access management and monitoring management can be carried out on the data of the industrial software system, the industrial software system can be maintained and managed in time, and the use effect can be improved.

The invention discloses an industrial software platform industrial cluster supply chain super network model construction and optimization method, and aims to better research an industrial cluster supply chain and enable the industrial cluster supply chain to obtain the minimum economic cost and time cost and the maximum supply chain anti-risk capability. Dividing the whole industrial cluster into a supply chain sub-network, a value self-network and a technical service sub-network to systematically optimize supply chain resource configuration; aiming at the problem that a traditional optimization modeling method is difficult to accurately evaluate the influence of a technical service network on factors such as the production value of a whole industrial cluster supply chain, a deep learning method is adopted to construct an input and output model of a technical service sub-network, and parameters output by the model are predicted to obtain supply chain parameters required by optimization; and in combination with a multi-objective optimization algorithm, the cost of the supply chain is minimized, the production cycle is minimized, the anti-risk capability is maximized, and an optimal supply chain scheduling strategy is sought.

The invention provides a process digital collaborative management method and system based on a data unified exchange platform, and belongs to the technical field of industrial software and intelligent manufacturing, and the method comprises the steps: constructing the data unified exchange platform, so as to achieve the data exchange among a process management system, a PLM system and a production management system; in the process management system, based on the EBOM obtained from the PLM system, constructing and managing a multi-state process BOM including a basic PBOM, a trial-production PBOM and a production PBOM; and a collaboration mechanism is constructed through a data unified exchange platform, so that business collaboration based on the multi-state process BOM is realized. According to the method, the problems of data consistency and flow collaboration which troubles the manufacturing industry for a long time are solved, and key technical support is provided for enterprises to realize digital transformation and intelligent upgrading through efficiency improvement, quality guarantee, cost control and knowledge precipitation.

The invention discloses a benefit distribution model construction and collaborative optimization method under multiple operation modes of an industrial software service platform, and the method comprises the following steps: 1, analyzing an optimal decision based on a Stackelberg master-slave game decision model: 11) constructing a pricing model, profit functions and constraint conditions of the industrial software service platform and the industrial software developer are obtained respectively; 12) performing optimal game decision analysis; 121) analyzing optimal decisions of an industrial software developer and an industrial software service platform under an independent operation condition to obtain an optimal price and a maximum benefit; 122) analyzing the optimal decision of the industrial software developer and the industrial software service platform under the cooperative operation condition to obtain the overall maximum benefit; 2, establishing a benefit distribution model based on a Shapley value method; step 3, benefit decision optimization: 31) fitting a relation function between the price and the number of functional modules by using historical data; and 32) adopting a reinforcement learning method to decide an optimal benefit distribution scheme.

The invention discloses a remote code injection prevention security protection method for industrial software, which belongs to the technical field of digital information transmission, and comprises the following steps: deploying a security protection client at an industrial equipment end and establishing a communication channel with a cloud server; obtaining a core code set of the industrial software, and protecting the core code set through the security protection client; when the industrial software runs, first environment characteristic data during running are collected through the safety protection client side, and abnormal behaviors are detected; and the cloud server obtains second environment characteristic data when the security protection client is started, and performs trust evaluation on the industrial equipment by adopting a multi-factor dynamic authentication strategy to obtain a total score of trust evaluation. According to the security protection method for remote code injection prevention of the industrial software, the problem that an existing industrial control system is low in security and reliability due to the fact that malicious operation carried out by an attacker in the operation process is difficult to deal with is solved.

The invention relates to the field of three-dimensional industrial software, in particular to a method and equipment for measuring a lightweight model in cutting simulation and a storage medium. Compared with the prior art, the technical scheme provided by the invention has the following beneficial effects: the correctness advantage is that the randomness of local sampling of triangular mesh vertexes in a traditional method is avoided through a method of geometric fitting of data in a continuous instruction of an NC code and analysis and extraction of processing features; the types and basic data of the processing features can be more accurately obtained. The processing speed has the advantages that the problem of low efficiency of identifying features according to a large amount of geometric information in a lightweight model in a traditional method is avoided through a method of geometric fitting of data in continuous instructions of NC codes and analysis and extraction of processing features, and the NC codes may already contain basic data of the features; and the process of calculating the feature size by the measurement function can be reduced in a manner of associating the triangular mesh with the feature basic data.

The invention discloses a multi-level service orchestration method for industrial software, which is particularly suitable for integrated development, deployment and management of industrial software application. The method focuses on arranging codes through dragging of a low-code page of a cloud platform, and performing automatic micro-service packaging and on-demand arrangement on the generated codes to form industrial software application. Constructing a mirror image for the micro-service application and carrying out containerization arrangement to form an application service capability; and finally, performing API-level arrangement on the atomic APIs provided by the service deployed by the container to form aggregated API capabilities. According to the method, each link from demand definition to final API service capability provision is integrally covered, especially for the field of industrial software, manual intervention is reduced, and the method is suitable for diversified application scenes.

Various embodiments of the teachings herein include an interface display method for industrial software. An example method includes: obtaining a present operation sequence by a user in a functional interface of industrial software; using a recurrent neural network (RNN) model to predict a next operation of the user according to the present operation sequence by the user, wherein the RNN model is obtained through history operation sequence of the user and history operation sequence of typical users in industrial domain knowledge; and highlighting a functional component of the predicted operation of the user from the functional interface and / or generating navigation corresponding to the predicted operation of the user.

The invention discloses a hierarchical control network architecture based on an industrial software defined network (SDN), provides a dual-time scale network slicing method and a multi-domain resource collaborative management and control scheme, and shows a remarkable industrial application prospect. The hierarchical control architecture allows flexible configuration of the SDN controller according to a specific industrial environment, and realizes multi-level resource management and control. And the SDN controllers of different levels can be dynamically adjusted according to actual requirements to adapt to complex and changeable industrial scenes. According to the dual-time-scale network slicing method, resource reservation and arrangement are decoupled, and the resource scheduling efficiency is improved. By monitoring the network state in real time, the resource configuration is optimized, and the heterogeneous QoS requirement is ensured to be met, so that the utilization rate of resources is remarkably improved.

The invention discloses a heterogeneous fuzzy and game-based double-layer adaptive evolution CAD modeling command generation method, and belongs to the technical field of artificial intelligence and computer-aided design intelligent modeling. The method solves the technical problems that in an existing natural language driven CAD command generation technology, fuzzy semantics are difficult to quantify and map, heterogeneous commands are poor in adaptability, the executable performance of generated commands is low, and the reasoning process is black-box-shaped. According to the method, a semantic analysis-game generation-evolution refinement-verification backtracking-knowledge precipitation full-process closed-loop architecture is constructed, the method can be applied to the fields of industrial software, three-dimensional modeling, intelligent design systems and the like, the engineering performability, generalization ability and reasoning interpretability of command generation are improved, the generation diversity and feasibility are balanced, and the method is suitable for popularization and application. And the model iteration cost is reduced, and technical support is provided for industrial landing of the text-driven CAD modeling technology.

The invention relates to the technical field of industrial software, and particularly discloses a three-dimensional model lightweight processing method and system in industrial software. The method comprises the following steps: associating data of a three-dimensional model to loadable parameters and reconfigurable parameters to execute differentiation processing on the three-dimensional model so as to generate a to-be-verified model, verifying the to-be-verified model through a verification port, and verifying the to-be-verified model according to three-dimensional parameter information causing a verification failure state. And regenerating a lightweight data packet for replacement operation or node data for reconstruction operation, and repeatedly executing the operation and verification process until lightweight is completed. While light weight processing is completed through replacement operation, high compression ratio is achieved through reconstruction operation, necessary model details are reserved, balance between light weight and model fidelity is achieved, errors in the processing process can be recognized and corrected, flow interruption caused by failure of single-time processing is avoided, and the processing efficiency is improved. And the functional integrity and availability of the output three-dimensional model are ensured.

The invention belongs to the field of software testing in software engineering, and particularly relates to a test case sorting method based on a multi-arm bandit algorithm, which can be used for optimizing the execution sequence of test cases and improving the fault detection efficiency. According to the method, the execution sequence of the test cases can be dynamically optimized, and the fault detection efficiency is remarkably improved. Through combination of coverage rate information and fault probability prediction, the test cases with high fault probability and high coverage rate can be executed preferentially, and waste of test resources is reduced. In addition, the method has high expandability and can adapt to industrial software systems of different scales and complexity.

The invention discloses a large-scale language model software operation and maintenance log analysis method based on meta-context learning, and the method comprises the following steps: S1, original log preprocessing, S2, log clustering and representative data set construction, S3, meta-context training data construction, S4, large-scale language model meta-context training, and S5, analysis reasoning data set construction. And S6, analyzing and reasoning a large language model log. By means of the method, an efficient, accurate and fast-adaptive solution combining offline element context training and online log analysis is provided for the log analysis task in the field of software operation and maintenance, the method is suitable for the log analysis task in an industrial software system, and the method has the remarkable advantage in analysis accuracy.

The invention discloses a comprehensive quantification method for collaborative benefit evaluation among enterprises in the technical field of enterprise evaluation, and the method supports enterprises to set evaluation index preferences according to an enterprise collaborative benefit evaluation index system, set importance degrees of indexes, calculate importance weights of all the indexes, and improve the evaluation efficiency of the enterprises. Generating evaluation preference templates for different types of resource cooperation enterprises; after the cooperative task is completed, the cooperative enterprise performs quantitative evaluation on the cooperative attitude and the cooperative benefit condition of the cooperative enterprise, and evaluation data is input into a cooperative benefit evaluation library by the industrial software platform; in combination with the importance weight of each index of the evaluation preference template and the evaluation data, the method provides auxiliary decision-making data support which can be dynamically updated and quantitatively compared for platform enterprise selection cooperative objects.

This invention discloses a phased anomaly detection method for ultra-large-scale industrial software supply chains based on graph neural networks. It divides the software anomaly detection task into two stages. In the rapid contamination stage, a simple model is used to quickly learn global anomaly patterns and filter out potential anomaly nodes and regions. In the precise detection stage, a more complex model is used to detect anomaly nodes. Furthermore, this invention defines the "disorder level" of nodes on the graph based on the Lyapunov coefficients in chaos theory and utilizes the "defect" of rapid oversmoothing in graph neural networks to rapidly propagate the behavioral patterns of anomaly nodes on the graph, simulating the propagation process of the entire chaotic system. In this way, this invention can quickly learn the global node anomaly patterns and quickly and accurately filter out candidate anomaly nodes and regions.

The present invention discloses a performance testing method and system for large amounts of industrial software big data. Among them, the performance testing method includes deploying an API request capture tool; simulating user access to the system under test, collecting API request information to generate a performance testing script; deploying a script management center tool for importing test cases, parsing the imported test cases, storing the parsed content in a use case maintenance table, and connecting to the database through a data testing script; deploying a test management center tool, including a message management center module for sending message tasks to the script management center, sending tasks for performance testing of large amounts of data; the performance testing script calls the data testing script, and the data testing script inserts test data into the database based on the status flag of the use case maintenance table, and the generated test data is used for performance testing of large amounts of data; for different business scenarios of industrial software, quickly create test data with large amounts of data.

The invention discloses an industrial software resource evaluation method and system based on an improved FAHP-TOPSIS method, and belongs to the technical field of industrial software resource evaluation. The method comprises the following steps: determining an evaluation index system of industrial software resources by using an AHP method; according to score comparison of a plurality of experts for the evaluation index system, obtaining a weight cognitive matrix of each expert for each industrial software resource evaluation index; and analyzing and determining the relative importance of the weight cognitive matrix endowed by each expert in the overall index system through a grey correlation analysis method, and forming a comprehensive weight value of each evaluation index. According to the method, the TOPSIS method is utilized, the industrial software resources are subjected to efficiency sorting and optimization according to the comprehensive weight values, judgment errors caused by professional background differences of experts are effectively avoided, the accuracy and reliability of evaluation are remarkably improved, complex evaluation indexes and interrelations are effectively processed, and the decision making process is made to be more reasonable.

The embodiment of the invention provides a data collaboration method and system and computing equipment, and the data collaboration system can comprise management software and a cloud platform which are deployed on client equipment. And on the client equipment, the butt joint between the management software and the standalone version industrial software is realized through the management software SDK. On the basis, processing logic used for achieving data collaboration is integrated in management software and a cloud platform in a distributed mode. A user can operate the management software on the client device to download the file on the cloud device to the client device, and automatically open the file by using the corresponding standalone version industrial software on the client device for editing; in addition, the file edited by the user through any standalone version industrial software can be automatically uploaded to the cloud platform through operation and triggering of the management software, additional user operation is not needed, the user experience is good, and the data collaboration efficiency can be improved.

The invention relates to the technical field of industrial internet, artificial intelligence and human-computer interaction, in particular to an industrial internet software intelligent customer service interaction method based on multi-modal deep fusion. According to the method, an intelligent customer service system integrating natural language processing, a voice technology, computer vision and augmented reality (AR) is constructed aiming at the characteristics of complex operation, multiple user groups, high problem speciality and the like in an industrial scene. The core of the method is that operation questions, interface states and potential faults of engineers in software use are accurately understood through multi-modal input; an accurate solution is generated in combination with the knowledge graph of the industrial software and the real-time data of the equipment; a 3D virtual expert image is driven, and immersive and scene-based guidance is provided by fusing voice explanation, interface marking guidance, AR superposition demonstration and operation demonstration videos. The method has the working condition self-adaptive capability, can identify the professional level (such as a green hand / expert mode) of a user, and adjusts the explanation depth; and cross-region collaboration is supported, a plurality of users are allowed to synchronously share a guidance picture of a virtual expert, and efficient remote collaboration troubleshooting is realized. The method solves the technical problems that traditional industrial software is slow in customer service response, abstract in guidance, difficult to process complex problems on site and the like, and operation and maintenance efficiency and user experience are remarkably improved.

The invention discloses a gadget polishing process intelligent decision system applied to CAM industrial software. The gadget polishing process intelligent decision system mainly comprises the following steps: establishing a set of instance expressions which can be identified and used by a computer; establishing a small tool polishing instance database according to the instance expression; performing standardization and anomaly detection on the data in the instance library; establishing a process reasoning module for realizing static mapping from multiple input features to multiple output parameters; training a process reasoning module by using data in the instance library; a new small tool polishing process problem is imported into the process reasoning module; inputting the optimal prediction process parameters into industrial software with a CAM function; the industrial software generates a tool path and a machine code; the numerical control machine tool or the robot completes actual machining of the workpiece according to the machine code; and new data introduced by the new problem is recycled and classified into an instance library according to the instance expression requirements.