1627results about How to "Reduce development costs" patented technology

The embodiment of the invention provides a humanoid robot flexible assembly method and system based on a thinking chain and a medium, and belongs to the technical field of natural language processing. According to the scheme, the assembly task instruction provided by the user is obtained, the assembly task instruction is subjected to task analysis through the thinking chain model, the weight of each word in the assembly task instruction is calculated through the attention mechanism in the analysis process, and the analysis target related to the current task is dynamically adjusted according to the weight; the task is decomposed into a plurality of subtasks layer by layer according to an analysis target, a task tree structure is obtained, the task tree structure is traversed to determine an assembly plan, the assembly plan comprises an execution arrangement sequence, task path planning and assembly action parameters, and the humanoid robot is controlled to execute assembly operation according to the assembly plan. The task information is automatically extracted by combining a hierarchical thinking chain and an attention mechanism and is converted into assembly operation, a clear execution plan is generated, and the flexibility of task analysis is improved.

The invention provides a multi-agent cooperation system, which comprises a multi-agent cooperation engine module, a task decomposition and distribution module and a tool calling normal form module, and is characterized in that the task decomposition and distribution module is used for decomposing tasks and distributing decomposed sub-tasks to agents; the multi-agent cooperation engine module is used for managing cooperation and communication between agents; and the tool calling module enables the intelligent agent to dynamically call different tools. According to the multi-agent collaboration system, the collaboration efficiency among the agents, the flexibility of task decomposition, the adaptivity of tool calling and the intelligent level of user interaction are remarkably improved, and remarkable technical advantages and effects are shown in complex task processing and dynamic environment processing.

The invention discloses a warehouse level code problem solving system based on a large language model, and relates to the technical field of software engineering, a source code file is extracted from a code warehouse, a static analysis tool is utilized to analyze codes, a warehouse level code graph is constructed, and then code entities and dependency relationships thereof are analyzed; based on the warehouse-level code graph, combining historical problem solving data, and constructing a dynamic evolution knowledge graph through continuous learning; through a context retrieval module, according to the context information of the current problem, dynamically retrieving related solution strategies and generating corresponding code patches, complementing or reviewing suggestions; the validity of the generated patch is verified through static analysis, unit testing and integration testing, and a problem solving strategy is optimized according to feedback of a developer. The problem solving efficiency and accuracy can be improved, the development cost is reduced, the solving process is gradually optimized through an intelligent feedback mechanism, a closed-loop feedback mechanism is formed, and therefore the increasingly complex software development requirements are met.

The invention belongs to the technical field of natural language processing. According to the zero resource framework-based fine-grained large model illusion processing method and system, multi-dimensional anomaly detection is carried out on a preprocessed text through a large language model, and a multi-dimensional anomaly detection result is obtained; performing anomaly correction priority ranking according to a multi-dimensional anomaly detection result; dynamically adjusting a context window range according to a syntactic analysis result and the generated syntactic dependency tree; performing priority correction on high-risk vocabularies and sentences according to an abnormal correction priority ranking result in combination with the adjusted context window range; and evaluating the corrected content, and obtaining a corrected final text when a termination condition is met. According to the method, the excessive confidence, subtle semantics and grammar deviation illusion detection of the detection content is realized, and the illusion detection processing capability of a large language model, especially the detection processing capability of a fine-grained illusion problem, is improved.

The invention discloses a query system, method and equipment based on conversion from a natural language to an SQL (Structured Query Language) and a medium. The query system comprises a data access layer used for receiving a natural language query request of a user; the preprocessing module is used for preprocessing the natural language input by the user to obtain the preprocessed natural language; the deep learning model is used for converting the preprocessed natural language into an SQL (Structured Query Language) statement; the optimization module is used for optimizing the SQL statement to obtain an optimized SQL statement; the database interface module is used for executing the optimized SQL statements and returning query results corresponding to the optimized SQL statements; and the application layer is used for displaying the query result. It can be seen that a large amount of manual data annotation is not needed, the system development and maintenance cost is reduced, the accurate SQL statement is rapidly generated through multi-step processing and the optimization algorithm, and the efficiency of converting natural language query into the SQL statement can be improved.

The invention discloses a financial data automatic filling method and device, equipment and a storage medium, and belongs to the technical field of big data. The method comprises the following steps: determining a data source corresponding to original financial data by using a pre-trained data classification model; acquiring a data processing rule engine and a target data filling template matched with the data source; performing data cleaning on the original financial data based on a data cleaning rule; performing data analysis on the standardized data by using a data analysis rule; performing association relation extraction on the target data source field and the target filling field by using a data mapping rule, and performing data field mapping according to the field association relation; and according to a data field mapping result, filling the target data source field into the target data filling template to generate a financial data report. The invention further relates to the technical field of block chains, and financial data are stored on the block chain nodes. According to the method and the device, the applicability and the operation convenience of automatic filling and reporting of financial data in a multi-source data scene are improved.

The invention provides a code analysis and generation method and system based on a large model. The method comprises the steps of obtaining a generation demand input by a user; based on the generation demand, determining a to-be-called target function module from a code function module set, and determining an execution sequence of the target function module, the code function module set comprising a code analysis module and a large model agent module with various code generation functions. And calling the target function modules in sequence based on the execution sequence to obtain a code corresponding to the generation demand. According to the method, the code snippets conforming to the specifications are automatically generated, so that the coding burden of developers is relieved, the development cost is reduced, and the development efficiency is improved. Meanwhile, a large model agent technology is efficiently utilized, and the analysis accuracy is improved through automatic code analysis. In addition, the system can adapt to different programming languages and development frameworks, and diversified development requirements are met.

The invention belongs to the field of artificial intelligence, and relates to a code conversion method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a source code and a target language; analyzing the source code to generate an abstract syntax tree; analyzing the abstract syntax tree, and extracting semantic information; according to the semantic information and a grammar rule of a target language, converting the code into the target language to obtain a preliminary conversion code; and further optimizing the preliminarily converted code to generate a final translated code. The automation degree is high, and the workload of manual conversion is greatly reduced; the conversion process is standard and unified, and the consistency and accuracy of code conversion are improved; by analyzing the AST, the source code can be understood more deeply, so that the translation code with higher quality is generated; extension and maintenance are easy, and support for new languages or characteristics can be added according to needs; the optimization step ensures that the converted code is not only correct, but also efficient and high in readability; and cross-language programming and code reuse are promoted.

The invention relates to the field of intelligent control and software management, and discloses a carrier rocket software control method and decision making device based on SAC, and the method comprises the following steps: obtaining the state information of a carrier rocket software system, and constructing a unified expression; an optimal action is generated based on an SAC algorithm; executing the action and collecting feedback information after the action is executed; utilizing feedback information to optimize an actor network and a commentator network in the SAC model, and continuously updating a decision strategy; based on the optimized strategy, the task priority, the running state of the software module and the abnormal recovery operation are dynamically adjusted, and the system state is collected again to optimize the model in a closed-loop mode. The system operation state can be adjusted in real time, the task scheduling efficiency, the resource utilization rate and the exception handling capacity are improved, intelligent management of a carrier rocket software system is achieved, and the method is suitable for complex task environments and high-dynamic operation scenes.

The invention discloses an intelligent glasses audio processing method and system. The method comprises the following steps: data preparation: collecting source domain data and target domain data; pre-training the model; converting the audio signal into sequence data; an RNN model is constructed and trained; model migration: migrating the pre-trained RNN model to target domain data, and processing noise in the target domain data by using parameters and features of the pre-trained model; intelligent analysis and processing, wherein noise in target domain data is analyzed and processed by using the migrated RNN model; real-time processing and feedback are carried out, audio signals captured by a microphone of the intelligent glasses are analyzed in real time, and a noise reduction strategy is dynamically adjusted to eliminate background noise; and optimizing noise reduction parameters and strategies according to the feedback. By collecting the source domain data and performing model pre-training, the RNN model can identify and process various known noise types and features and quickly adapt to different noise environments, so that the noise reduction effect is improved, and a user can obtain the optimal hearing experience in different noise environments.

The invention relates to the technical field of industrial Internet of Things, in particular to a multi-protocol multi-device access system and method based on an industrial Internet of Things platform. Comprising a plurality of protocol parsers used for realizing device access and multi-protocol parsing; the low-code data format conversion module is used for converting the data analyzed by the plurality of protocol analyzers into a data format unified by an industrial Internet of Things platform; and the data with the uniform data format is reported to an application end of the industrial Internet of Things platform after being processed. Compared with the prior art, the system and the method have the following remarkable technical effects that the system and the method can support equipment access of various protocols, and data format conversion is completed by adopting low-code data format conversion. The flexibility and the convenience of equipment access are improved, the development and maintenance cost is reduced, and the compatibility and the expandability of an industrial Internet of Things platform are remarkably improved.

The invention discloses a CTP battery in-box clamping force calculation method, device and equipment and a medium, and relates to the technical field of battery production, the method comprises the following steps: constructing a stress model when a block is clamped and suspended, analyzing to obtain an arc line where stress centers of all battery cells are located, and calculating the length of the arc line where the stress center of a single battery cell is located; analyzing the stress condition of the central battery cell in the stress model, calculating to obtain the transverse theoretical clamping force of the clamping jaw, and calculating to obtain the actual clamping force needing to be provided by the clamping jaw; based on the bearable pressure value of the battery cell and the transverse theoretical clamping force of the clamping jaw, the actual clamping force is increased or decreased through a preset control strategy. According to the invention, the battery cell can be effectively prevented from falling and being clamped and damaged.

The invention relates to a high-toughness corrosion-resistant aluminum alloy and a preparation method thereof. The high-toughness and corrosion-resistant aluminum alloy is prepared from the following components in percentage by mass: 3.5 to 4.6 weight percent of Mg, 1.3 to 1.5 weight percent of Zn, 0.1 to 0.4 weight percent of Sc, 0.4 to 0.5 weight percent of Zr, 0.4 to 0.5 weight percent of Cu and the balance of Al. The optimized preparation method comprises the steps that aluminum alloy mixed powder serves as a raw material, laser additive manufacturing is adopted, and a printed product is obtained through printing; during printing, the laser power is controlled to range from 250 W to 350 W, the laser scanning speed ranges from 800 mm / s to 1200 mm / s, the laser spot diameter ranges from 60 micrometers to 80 micrometers, the scanning interval ranges from 0.02 mm to 0.05 mm, and the powder laying thickness ranges from 0.03 mm to 0.05 mm. Based on a cross alloy design strategy, the in-situ alloying powder manufacturing technology and the laser powder bed melting technology are combined, collaborative optimization of the mechanical property and the corrosion resistance is achieved, meanwhile, the material development and preparation cost is effectively reduced, and the application requirements of multiple fields are met.

The invention relates to the technical field of intelligent bed structures, in particular to a three-dimensional parameterized simulation design method for an intelligent bed structure. The method comprises the following steps: constructing a three-dimensional structure model based on an intelligent bed design structure to obtain an intelligent bed three-dimensional structure model; performing body type dynamic structure support analysis on the intelligent bed three-dimensional structure model, and performing self-adaptive adjustment mechanism design to obtain an intelligent bed self-adaptive adjustment mechanism; error parameter balance optimization is carried out on the intelligent bed self-adaptive adjustment mechanism, optimization adjustment is carried out, and an intelligent bed self-adaptive adjustment optimization mechanism is obtained; performing adjustment simulation mechanism model construction on the intelligent bed adaptive adjustment optimization mechanism to obtain an intelligent bed adjustment simulation mechanism model; and sending the intelligent bed adjustment simulation mechanism model to an intelligent bed control unit to execute intelligent bed parameterized simulation design. The intelligent bed structure technology is optimized, so that the intelligent bed structure technology is more perfect.

The invention discloses a low-code management system and method based on the AIot Internet of Things, and relates to the technical field of code management, the system comprises a template learning optimization module, a component dynamic adaptation module, a demand conversion module, a code annotation generation module and a code compatibility conversion module; the template learning optimization module is used for automatically extracting optimization points and updating template settings by analyzing historical cases and user data so as to improve the applicability of the template in practical application; according to the invention, through the template learning optimization module, deep analysis of historical cases and user data is realized, optimization points are automatically extracted, template setting is updated, the applicability and development efficiency of the template in practical application are remarkably improved, and through the automatic template updating process, manual intervention is reduced, the development cost is reduced, and the development efficiency is improved. And meanwhile, the stability and compatibility of the system are kept.

The invention provides a production information integration system based on visual configuration. Comprising a component management module used for providing a visual configuration interface; the data source management module is used for managing a data source component and configuring and verifying a data source according to input data source connection parameters; the data processing module is used for processing the data acquired by the data source management module according to the operation logic configured by the component management module; the target data management module is used for receiving and storing the data processed by the data processing module; the job scheduling module is used for scheduling the jobs configured by the component management module according to a preset scheduling rule; and the monitoring and diagnosis module is used for monitoring the operation state of each module in real time, recording the operation execution log of each module, detecting the abnormal condition in the operation of the system and generating a diagnosis report and a troubleshooting suggestion. The system development cost can be reduced, the system development period can be shortened, and the expandability and compatibility of the system can be improved.

The invention relates to an intelligent medical question-answering system based on a large model, and belongs to the technical field of intelligent medical question-answering. The system comprises a basic module which is used as middleware between a user and the LLM and serves as a routing gateway, and through the basic module, the system can be connected to any supported LLM without modifying any code; the function module is used for analyzing the questions asked by the user, constructing proper prompt words to ask the LLM, formatting the response of the LLM and returning the formatted response to a system user interface; the memory module is used for realizing association between the case data of the user and the historical dialogues of the user by means of a vector database; when the big model is helped to answer, specific answers can be made to specific users, and meanwhile, historical dialogue information is owned; and the safety module is used for carrying out safety prompt.

The invention discloses a multi-language dynamic switching system and method of embedded equipment, and relates to the technical field of human-machine interfaces and embedded systems.The method comprises the steps that source codes in an HMI program are traversed through a character extraction module to extract all character strings; eliminating repeated items, numbering the character strings, and outputting a database file and a replacement array; substituting the serial number into the source code file again by using the substitution array to replace the character string at the corresponding position; outputting the translated database file as a C language source code, compiling the C language source code into a dictionary generation module project, generating an executable file, and outputting the dictionary file to an HMI running directory after the device is started; after the HMI program is started, the dictionary is read into different dictionary arrays according to blocks, and character strings at corresponding positions in the dictionary arrays are extracted after a page sends a request. The method has the advantages of being lower in maintenance difficulty, more efficient in resource utilization, shorter in indexing time, better in system robustness and the like.

The invention provides a large model connection context dialogue method based on an LSTM (Long Short-Term Memory) model, which belongs to the field of intelligent deep learning and comprises the steps of data preprocessing, input sequence coding, hidden state and memory cell initialization, decoding response generation, output sequence decoding, training and optimization, evaluation and adjustment. The method has high accuracy and response speed when processing a complex dialogue scene, and can be widely applied to the fields of intelligent customer service, intelligent assistants and the like.

The invention discloses a general image evaluation method based on a hybrid expert model, which can be applied to various image evaluation scenes to automatically evaluate the quality or aesthetics of images. According to the method, a visual encoder and a text encoder are used for extracting visual and text features of an image respectively, and a quality or aesthetic score is calculated through an adapter. The visual encoder and the text encoder are integrated with a hybrid evaluation expert module, the hybrid evaluation expert module combines a shared evaluation expert and a self-adaptive evaluation expert, the shared evaluation expert is responsible for learning general evaluation knowledge, the self-adaptive evaluation expert dynamically learns specific data sets, expert contribution is adjusted through a router, and labeling deviation in multi-data-set training is effectively managed. Furthermore, a scene-based difference prompt strategy is implemented, the model adaptability is enhanced through a scene specific prompt word, the annotation deviation between data sets is reduced, and the evaluation accuracy is improved. The method not only improves generalization and adaptability of the model, but also optimizes parameter efficiency and training efficiency, and has a wide application prospect.

The invention provides a virtual training and evaluation method and device for intelligent focusing of an operating microscope, and relates to the technical field of intelligent control of operating microscopes, and the method comprises the steps: constructing a virtual operation scene based on real operation video data; obtaining a current virtual microscopic texture image which is collected by the virtual camera at the current position and aims at the virtual operation target; determining a region of interest in the current virtual microscopic texture image, and calculating the definition of the region of interest; based on the definition of the region of interest, controlling the virtual camera to perform focusing motion to obtain a focusing distance after the position of the virtual camera is updated; and calculating performance index data based on the real focusing distance and the updated focusing distance so as to evaluate the focusing algorithm of the operating microscope based on the performance index data. According to the method, organic unification among virtual operation scene depth, microscope motion control and image quality evaluation is realized, and a safe and reliable quantitative evaluation means is provided for development of an intelligent focusing algorithm of the operation microscope.

The invention relates to the field of automation, the field of artificial intelligence and the technical field of code quality management, and provides an automatic online code review method and system. The method comprises the following steps: compiling a self-defined rule description document, and compiling or updating a formulated coding specification document; a webhook request is set in a code warehouse in advance and used for automatically triggering an external service or process when a specific event occurs in the code warehouse; compiling or modifying codes, and manually / automatically submitting the codes to a code warehouse; the code warehouse automatically triggers a pre-configured webhook request, and then an interface of an AI code review system is called for code review; the AI review system pulls the to-be-reviewed code, performs rule matching on the to-be-reviewed code according to preset rule template configuration, detects whether a code violating a rule exists or not, and generates a review report after review is completed; wherein the rule template configuration comprises a rule description document and a coding specification document.

The embodiment of the invention relates to a database query method and device, equipment and a storage medium, and the method comprises the steps that a database query request comprising template identification and query configuration information is received, the template identification is used for identifying a target query template, and the query configuration information comprises information for query parameter configuration in the target query template; obtaining a target query template from a template library based on the template identifier; generating a target query statement of the target database based on the target query template and the query configuration information; and executing the target query statement for the target database to obtain a query result corresponding to the database query request. Therefore, according to the database query method provided by the embodiment of the invention, when the database query requests from different business parties are received, the corresponding target query statements can be generated based on the query configuration information in the database query requests and the target query templates corresponding to the template identifiers, and the target query statements are used for processing the database query requests.

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 provides a passing noise prediction method and device for an engineering vehicle, and relates to the technical field of engineering vehicles. The method comprises the following steps: carrying out sound source division on an engineering vehicle physical model to obtain a vehicle noise source; carrying out position division on a noise passing rapid acceleration area of the engineering vehicle to obtain working condition point locations; performing vehicle parameter calculation based on the vehicle noise source and the working condition point location to obtain rotating speed data and torque data; on the basis of the rotating speed data and the torque data, according to basic data of the engineering vehicle, characteristic data are collected through an acoustic test, and acoustic characteristics of a sound source are obtained; based on the vehicle noise source, noise transfer calculation is carried out according to the engineering vehicle basic data, and a noise transfer function is obtained; and performing transient noise correlation prediction according to the acoustic characteristics of the sound source and the noise transfer function to obtain a passing noise prediction result of the engineering vehicle. The method is an efficient and accurate noise prediction method for the engineering vehicle.

The invention provides an intelligent agent automatic generation method and system based on a large language model. The method comprises the steps that a page is created according to a component requirement and a page requirement input by a user, a component is generated, and configuration parameters are set; analyzing the interface document through a large language model, generating page components according to the component knowledge base, and binding and combining the page components, configuration information of the components and interaction behaviors through a translator; initializing a configuration interaction behavior; configuring behavior conditions of the interaction behaviors, selecting data sources of the components, and modularly rendering a system page according to configuration information of the page components; generating a page layout according to the generation mode of the combined page of the business requirements and the input natural language and the page generation mode, and binding the interactive behavior information to generate a system page; and customizing the storage mode of the intelligent agent. The method depends on a memory unit mechanism, is driven by a large model, assembles personalized application pages according to requirements and automatically executes task requirements, and has a higher-level automatic generation technology.

The invention relates to the technical field of artificial intelligence and natural language processing, and discloses an artificial intelligence-based platform dialogue method, which comprises the following steps of: obtaining API (Application Program Interface) information associated with a user question; constructing an LLM model comprising a plurality of model tasks, and training and reasoning the LLM model based on API interface information; assembling the user problems into cue words, inputting the cue words into an LLM model, and predicting user intentions and slot positions according to the user problems to obtain corresponding API data; and carrying out factual verbal skill description on the API data by using an LLM model to obtain and output a conversation verbal skill. According to the method, the requirements of a user can be quickly understood in a context multi-round dialogue mode, results generated by various tasks based on the LLM model can be mutually verified through a known API, the understanding ability, the generation ability and the controllability of the large model are fully exerted, and the BI platform is optimized and reconstructed through the LLM model. The development cycle of the project can be effectively shortened, the project development cost is reduced, and the universality of the BI platform is enhanced.

The invention discloses a work order auditing management method and system based on a large language model and a medium. The method comprises the steps of receiving input and correspondingly matching a work order auditing rule; based on the matched work order auditing rule, analyzing and processing the input through a large language model NLP, and converting the input into a natural language rule; carrying out synonym and antonym conversion on input white words, and carrying out equal rule conversion on converted words based on a corpus word library; performing rule text and code conversion on the converted natural language rule; and carding the processed rules into SQL statements which can be identified by the system according to the fields and the judger rule base. Updating the rule knowledge base; and writing the processed SQL statements which can be identified by the program into a rule knowledge base. According to the method, non-technical personnel can adjust and modify the work order information verification rule by using a natural language by using a large language model technology, so that the intelligent level of a work order system is enhanced, the development cost of the system is reduced, the auditing efficiency of a work order process system is improved, and the method has relatively good practicability.

The invention relates to the technical field of tire simulation design, in particular to a tire pattern longitudinal groove edge optimization design method and system and a computer program product. The method comprises the following steps: extracting actual grounding pressure data of a tire; establishing a pattern block geometric plane model, and applying proper boundary conditions; according to the condition of non-uniform grounding pressure distribution, the shape of the contact surface of the pattern block is adjusted to realize uniform distribution; the grounding pressure distribution of the edge of the pattern block is optimized by minimizing a grounding pressure skewness value; and finally, calculating and adjusting the outer contour curve of the tire to realize final optimization design. According to the method, the road holding force, the controllability and the driving stability of the tire can be remarkably improved, the service life is prolonged, the modeling and calculation time is relatively low, and the design and development cost is remarkably reduced.

The invention provides a calculation method, device and equipment for a heat transfer coefficient of a railway vehicle and a medium, and the method comprises the steps: for each structure model in a thermal resistance model of the railway vehicle, calculating a plurality of heat transfer layer thermal resistance values corresponding to the structure model, and calculating a structure thermal resistance value of the structure model based on the plurality of heat transfer layer thermal resistance values; for each area model, calculating the area thermal resistance value of the area model based on the structure thermal resistance values of the plurality of structure models corresponding to the area model; and calculating the total thermal resistance value of the rail vehicle based on the regional thermal resistance value of each regional model, and calculating the heat transfer coefficient of the rail vehicle based on the total thermal resistance value. By means of the method and device, the efficiency and accuracy of calculating the heat transfer coefficient of the railway vehicle are improved, and the research and development period can be shortened.