231 results about "Resource integration" patented technology

The invention provides a lightweight digital human lesson preparation system based on an intelligent agent, and belongs to the field of intelligent teaching. Through collaborative operation of four core modules of knowledge graph construction and reasoning, multi-modal cognitive agent, lightweight digital human generation and intelligent teaching plan assistance, the problems of low efficiency of resource integration, teaching content homogenization, insufficient digital human interaction experience and the like in traditional lesson preparation are solved. The knowledge graph construction and reasoning module is used for constructing a structured knowledge graph and realizing knowledge point association mining and teaching logic reasoning; the multi-modal cognitive agent module is used for generating personalized explanation content according with a teaching target by fusing multi-modal courseware analysis, semantic understanding and lecture style dynamic adaptation functions; the lightweight digital human generation module is combined with model pruning and emotion modeling technologies to synchronously output natural voice and a high-simulation digital human image; the intelligent teaching plan auxiliary module helps the teacher to intelligently generate a teaching plan and a teaching outline according to the courseware content based on the knowledge graph.

The invention relates to the technical field of online network teaching, and discloses an online network teaching practice method based on a virtual technology, and the method comprises the steps: obtaining virtual scene data, and carrying out the integration and building of a dynamic knowledge graph and a multi-modal teaching resource library; learning behavior features are extracted according to the cognitive features of the learner, knowledge difficulty levels are divided, and immersive learning is guided; constructing a cognitive evaluation model, optimizing a learning path through hierarchical reinforcement learning, and generating a self-adaptive teaching instruction; collecting interaction track data to generate an operation thermodynamic diagram, and evaluating the skill mastering degree; complexity parameters of the virtual scene are dynamically adjusted, a virtual-real linkage experiment simulation environment is constructed, and skill training closed-loop feedback is achieved. The invention further relates to specific implementation modes of resource integration, cognitive feature extraction, learning path optimization and the like. According to the method, the personalized and immersive experience and skill training effect of online teaching are improved, and the method is suitable for the field of online network teaching.

The invention relates to the technical field of cloud computing resource migration, in particular to a cross-cloud resource migration method and system, and the method comprises the following steps: carrying out the proportional distribution of the processing capability of a virtual machine instance based on the processing capability of the virtual machine instance, the block storage read-write rate and the network delay time, carrying out weighted average on the block storage read-write rate and the network delay time, integrating the block storage read-write rate and the network delay time into a two-dimensional matrix, sorting and removing low-threshold nodes, and generating a preliminary resource optimization data set; according to the method, resource optimization accuracy is improved through proportional allocation of virtual machine processing capacity, weighted average block storage read-write rate and network delay and performance index matrix processing, performance weight difference and path parameter optimization performance dependency expression are combined, node transmission efficiency and priority are dynamically adjusted, transmission path elasticity is enhanced, and resource optimization efficiency is improved. The resource state of the target end is monitored and adjusted in real time, migration stability is ensured, the resource integration precision and service controllability of the multi-cloud environment are improved, and complex application requirements are met.

The invention discloses a knowledge and data fused chronic disease collaborative management method and system, and relates to the technical field of medical management. Comprising the following steps: generating a feature data set, processing the feature data set by using a feature gain modeling method, and generating an enhanced feature data set; dynamically generating a chronic disease comprehensive knowledge graph, performing node supplementation and weight optimization on the knowledge graph, and generating a specific knowledge sub-graph of the patient; carrying out multi-time sequence prediction by utilizing the specific knowledge sub-graph, and carrying out short-term and long-term trend prediction; a patient health track model is constructed through the state transition probability in combination with the prediction result; according to the dynamic risk labeling result of the health track model, the health state of the patient is comprehensively evaluated in combination with the specific knowledge sub-graph, and personalized management suggestions are generated according to the evaluation result. According to the collaborative management platform, cooperation between different medical institutions and medical staff can be promoted, information sharing and resource integration are realized, and the overall level of medical services is improved.

The invention discloses a hyper-converged server multi-resource integration system and a scheduling method, belongs to the technical field of computer resource management, and aims at solving the problems that a traditional hyper-converged server is dispersed in resource scheduling, low in storage efficiency, difficult to identify abnormities and the like. Multi-source data is obtained by means of distributed acquisition nodes, a distributed time sequence database architecture is adopted for storage, a standard data set containing a multi-dimensional index is constructed, and efficient storage and rapid retrieval of the data are achieved. Positioning target data to construct a graph model, building a layered and partitioned distributed graph database, and capturing change events in real time to realize dynamic updating. And mining a resource causal relationship by using a graph neural network, and screening an effective causal chain. Meanwhile, according to a graph model, monitoring weights of nodes and connecting edges are calculated, differential monitoring is implemented, potential abnormal points are accurately identified, and a resource scheduling strategy is generated in combination with a causal relationship. Real-time feedback adjustment and database updating are performed during execution, multi-resource deep integration and intelligent scheduling are achieved, and the resource utilization rate and stability of the system are remarkably improved.

The invention discloses an intelligent private cloud resource scheduling management system based on a hyper-converged architecture, particularly relates to the technical field of virtual resource scheduling management, and is used for solving the problem of overall utilization rate reduction caused by cascade resource oscillation and fragmentation due to fault recovery in the prior art. Candidate buffer nodes are screened based on the resource occupancy state, a migration priority queue is generated in combination with the node resource isomerism matching degree and the real-time load, and an independent buffer resource pool is reserved at a target node; key resources are directionally reserved through resource locking labels, task dependence periods are associated, and task conflict risks possibly triggered after the resources are released are dynamically predicted; adjusting a label effective period to cover a task execution window based on a conflict identification result; and removing the label in the preset time window, releasing the resources to the buffer pool, synchronously triggering global resource integration and candidate node list updating, and realizing efficient recovery and dynamic redistribution of fragmented resources, thereby inhibiting multi-node resource fluctuation.

The invention provides a low-altitude three-dimensional traffic road network division method and system. The method comprises the following steps: S1, data acquisition and processing: performing real-time data acquisition and processing on low-altitude three-dimensional traffic road network division areas; s2, dynamically dividing the processed data to obtain divided logic data, and integrating low-altitude air traffic data and low-altitude road surface data to form three-dimensional traffic data; s3, performing structured processing on the dynamically divided logic data and the multi-modal resource integrated three-dimensional traffic data; obtaining a structured result of the low-altitude three-dimensional traffic road network; and S4, calculating a corresponding action specification based on a structured result. Efficient utilization and intelligent management of low-altitude traffic resources are realized through the steps of dynamic division of a low-altitude three-dimensional area, multi-mode resource integration, structured management, intelligent operation standardization and the like.

The invention provides a classical music cross-modal high-reliability experimental data set construction method for full-scene teaching application, and the method comprises the steps: constructing an original data set through collecting audio, video, music score and other multi-modal data, carrying out the standardization of each modal data through a preprocessing technology, and obtaining a multi-modal data set in a unified format; according to the semantic feature vectors, a knowledge graph is constructed, nodes represent semantic relations between music elements, edges represent semantic relations between the elements, a graph convolutional network is adopted to encode the knowledge graph, and semantic representation of the teaching content is obtained; a virtual reality rendering technology is adopted, multi-modal teaching resources are integrated into an immersive teaching scene, a content sequence is presented in real time, and dynamic teaching experience is obtained; and continuously collecting interaction data of the learner, updating the knowledge graph and the semantic feature vector, and optimizing the cross-modal fusion model by adopting an incremental learning algorithm to obtain a self-adaptive teaching data set.

The invention relates to the technical field of teaching management, discloses an intelligent teaching system based on a knowledge graph and virtual simulation and a construction method, and aims to effectively solve the difficulty in integration and management of teaching resources and provide personalized teaching experience. The system comprises a knowledge graph construction module which is used for integrating and standardizing teaching resources and constructing a knowledge graph containing knowledge points and association relationships thereof; the virtual simulation teaching module is used for displaying knowledge points in a virtual simulation form by constructing a knowledge presentation model and an interactive learning model, and evaluating learning conditions of students; the intelligent teaching control module is used for constructing a teaching strategy model by adopting a reinforcement learning algorithm and dynamically adjusting a teaching instruction according to the learning progress and the interaction effect of the students; and the execution presentation module is responsible for receiving and executing the teaching instruction and presenting the teaching content in the virtual teaching environment. The system improves the pertinence and effectiveness of teaching, and stimulates the learning interest of students.

The invention discloses a multi-cluster heterogeneous computing power scheduling method oriented to an AI training reasoning task, and belongs to the technical field of computers. According to a scheduling mechanism with issuing of the AI task as a core, the state of a resource pool is monitored in real time, resources are dynamically allocated, it is ensured that the AI task is efficiently executed, the use efficiency of the whole resource pool is improved, and the load state of an AI application example is detected in real time; through an elastic telescoping function, AI application examples are automatically expanded and shrunk, stable operation of AI tasks is ensured, resources of a plurality of clusters are integrated into a unified resource pool, centralized management and cooperative scheduling of the resources are achieved, the load condition of each cluster is monitored in real time, load balancing is automatically carried out among the clusters, and the AI tasks are reasonably distributed to different clusters. And the GPU resources of the specified model are accurately allocated to the AI task according to the actual demand of the AI task, so that the situation that the AI task application runs on non-optimal resources is avoided, and the efficient running of the AI task is ensured.

The invention provides a potential information identification method and system in combination with a heterogeneous network environment, and the method comprises the steps: firstly obtaining a heterogeneous network environment data set of a target network environment, which comprises original information resources of various data formats, and carrying out the feature extraction of the heterogeneous network environment data set to obtain an associated feature set; performing hierarchical pattern matching processing on the associated feature set based on a pre-trained information pattern analysis model to generate a potential information identification result set containing significant value information resource attribute identifiers and distribution paths, and generating a dynamic resource integration strategy according to the potential information identification result set; and the resource management system is used for adjusting a resource scheduling rule and a storage path mapping relation, finally feeding back a dynamic resource integration strategy to the data management system, triggering a resource directory updating operation, and improving heterogeneous network resource utilization and management efficiency.

The invention relates to the technical field of intelligent power grids, in particular to a collaborative optimization system and method for distributed power resources, and the system comprises a data collection module, a time-space correlation analysis module, a user response module, a data privacy protection module, a time-of-use electricity price optimization module and a scheduling control module. The space-time correlation analysis module performs interval prediction of distributed photovoltaic power by using a Bayesian-long short-term memory network model, the user response module designs a hierarchical response strategy according to user categories, and the data privacy protection module protects power utilization data through a differential privacy mechanism. The time-of-use electricity price optimization module dynamically adjusts the electricity price according to the peak-valley load difference, and the scheduling control module achieves the cooperative regulation and control of distributed resource integration, local-level power grid coordination and provincial-level power grid optimization. The technical problems of an existing power system in the aspects of efficiently integrating distributed resources, improving scheduling flexibility and optimizing an electricity price strategy are solved.

The invention provides an integrated sharing method and system for digital media content resources, and is applied to the technical field of data processing. The method comprises the following steps: preprocessing digital media content resources, cloud platform basic data and user demand data to generate a standardized media content sequence, cloud platform features, user demand features and resource classification features; processing the resource association relationship data to generate resource association features; processing a strategy in the shared strategy updating mechanism information based on the resource value evaluation system, and generating updated shared strategy data; processing the standardized media content sequence, the cloud platform feature and the resource association feature based on a target integrated sharing model to generate a target integrated sharing feature; and processing the target integration sharing feature based on the target integration sharing model and the updated sharing strategy data to generate a digital media content resource integration sharing result.

The invention discloses an automatic order sending recommendation integration system based on a knowledge graph, and relates to the technical field of intelligent recommendation and resource integration. The problems of poor data real-time performance, model staticization, low recommendation accuracy and the like exist in the prior art. According to the invention, operation and maintenance targets, fault features, resource attributes and historical cases are integrated in real time through a dynamic knowledge graph construction module, and a hierarchical multi-modal knowledge network is constructed; the conflict detection module is combined with a rule engine and a probability graph model to detect logic and semantic conflicts; the real-time inference engine realizes multi-constraint path search and multi-target optimization recommendation based on a graph neural network, and outputs a combination scheme of engineers, spare parts and tools; and the feedback closed loop module updates the knowledge graph and recommendation strategy parameters through a reinforcement learning algorithm. The method breaks through the limitation of a traditional static model, remarkably improves the real-time performance, accuracy and dynamic adaptability of order sending recommendation, and is suitable for scenes of enterprise I operation and maintenance, intelligent customer service, logistics scheduling and the like.

The invention discloses an intelligent production management order arrangement system, which belongs to the technical field of production management, and comprises an order management module, an intelligent production arrangement module, a resource integration and optimization module, a real-time monitoring and adjusting module and a data analysis and decision support module, through setting an order management module, an intelligent production scheduling module and a resource integration and optimization module, the order management module manages and grades orders during use, and the intelligent production scheduling module produces an optimal production schedule according to the order and equipment information and an algorithm. The resource integration and optimization module intelligently distributes resources by means of detailed modeling and analysis of resources such as equipment information and manpower, maximum utilization of the resources is achieved, the efficiency of production management scheduling is greatly improved, and the requirements of industry production can be met according to different equipment information.

The invention discloses a cross-domain data fusion method and system for distribution network middle stations based on a knowledge graph, and particularly relates to the technical field of data processing and fusion based on the knowledge graph. Firstly, intelligent gateway equipment is adopted to collect operation, fault, maintenance and other data of all systems and all lines related to distribution network operation, the data are transmitted to a data processing and storage module through a 5G communication protocol port transmission technology, and data cleaning, data integration, data conversion and other operations are carried out; classifying and storing the data, and then constructing a mapping knowledge domain in the distribution network; according to the method, a 5G communication protocol port transmission technology and a composite model are specifically adopted to perform knowledge extraction and knowledge fusion of graph construction, so that the technical problems of data islands and other data processing in a distribution network are solved, data sharing, service sharing and resource integration are effectively realized, and the reliability of the distribution network is improved. The problem that data between distributed systems in the current distribution network field is difficult to cooperate is solved, semantic integration and sharing of data between multiple systems are achieved, and the operation and maintenance efficiency and the intelligent level of a power distribution network are improved.

The invention discloses a case resource integration data system based on big data analysis, and belongs to the technical field of medical data. The method comprises the following steps: acquiring hospital case data and corresponding disease type data to construct a resource integration range, acquiring a personal case information set provided by medical consultation of a patient, performing sensitive data extraction on the personal case information set to obtain a dynamic case parameter set, and sending the dynamic case parameter set to a data risk analysis module; the multi-source data acquisition module processes the personal case information set as follows; according to the method, a data integration-risk analysis-clinical intervention closed-loop system is constructed, preorder data standardization integration guarantees analysis reliability, accurate risk analysis provides a direction for intervention, multi-level alarm and pre-plan matching is achieved through linkage of the preorder data standardization integration and the accurate risk analysis, prediction diagnosis reports and intervention suggestions are automatically generated, invalid operations are reduced, the clinical decision-making efficiency is improved, and the system is suitable for large-scale popularization and application. And meanwhile, through dynamic threshold updating and system self-iteration optimization, the adaptability and practicability of the system are continuously enhanced.

The invention discloses an intelligent database switching method and device, computer equipment and a storage medium, belongs to the technical field of big data, and is applied to a financial database downtime processing scene. According to the method, the node state of the database is monitored in real time, multi-dimensional load evaluation is combined, the optimal main and standby database combination is automatically selected, second-level fault sensing and rapid switching are achieved, and a minute-level time window needed by traditional database recovery is remarkably shortened. The database identifier is embedded in the service main key, so that the request can accurately position the corresponding database node, complex cross-database query and routing overhead are avoided, and the system response efficiency is improved. In addition, through data seamless transmission and automatic abnormal switching between the main database and the standby database, the risk of service interruption caused by database faults is reduced. According to the scheme, the high availability and the fault recovery speed of the database are improved, repeated construction of a multi-product-line database cluster is reduced through resource integration, and a large amount of hardware and operation and maintenance cost are saved.

The invention discloses a task scheduling method and device, equipment and a storage medium. The method is applied to a task scheduling cluster, and the task scheduling cluster comprises a first node and a second node. The method comprises the steps of obtaining a first task; the first resource quantity required by the first task is greater than the respective residual resource quantity of the first node and the second node; determining a second task executed by the first node according to the first resource quantity and the respective residual resource quantities of the first node and the second node; the second resource quantity required by the second task is less than the residual resource quantity of the second node, and the sum of the second resource quantity and the residual resource quantity of the first node is greater than the first resource quantity; and migrating the second task to the second node for execution, and executing the first task on the first node. In this way, the effect of fragment resource integration can be achieved, and therefore the utilization rate of computing resources is increased.

The invention discloses a digital country terminal service method and system based on a cloud service technology, and belongs to the technical field of digital country service. A framework of'government support + marketization operation 'is adopted, and co-construction of a design party, a service party and an operator is achieved. The design party is mainly responsible for overall planning and resource integration, the service party provides policy support and supervision, and the operator is responsible for specific platform research and development and daily operation; a database, network and software multi-layer architecture based on cloud service is adopted to support an innovative operation mode and an efficient data management strategy; meanwhile, deep integration of online cloud service platform enabling and offline services is emphasized, users are attracted through free content services, and paid value-added services such as online data security channel service charges are introduced, so that sustainable development of the service station is ensured; besides, key agricultural data are collected through multiple channels, and a data updating and optimizing mechanism is established, so that the data quality is improved, and powerful data support is provided for agricultural production and operation.

The invention provides a scientific and technological intelligence resource integration method based on a real-time data stream, which comprises the following steps: monitoring the task queue depth of each node through a load migration trigger condition, and if the queue depth of a certain node exceeds a preset upper limit value, starting a task migration process, selecting a target node with the shortest response time as a migration object in combination with the node response time statistical data, and obtaining a load redistribution instruction; and according to the load redistribution instruction, a processing result synchronization mechanism is adopted to coordinate the data processing progress among the nodes, if a source node completes task migration, a global task state table is updated, if a target node successfully receives a task, a confirmation signal is sent, and a closed-loop control mechanism for cooperative processing among the nodes is formed.

The invention belongs to the technical field of digital economy monitoring, and particularly relates to a digital economy monitoring method based on a block chain technology, and the method comprises the following steps: 1, constructing a digital economy co-creation platform based on a block chain, and enabling the digital economy co-creation platform to comprise a plurality of participants and a supervisor; 2, a plurality of participants jointly generate values through resource integration in the value co-creation process based on the digital economic co-creation platform, and a value network structure is constructed based on a block chain; 3, in the value network structure, dividing a value co-creation process into a value resource selection stage, a value resource acquisition stage and a value win-win stage through analysis and function definition of a resource subject; and step 4, the supervisor supervises whether the establishment process of the digital economic co-creation platform and the value co-creation process of the plurality of participants based on the digital economic co-creation platform are abnormal in real time. According to the invention, all participants share innovative resources, and the resource sharing safety performance is high.

Embodiments of the invention disclose a resource management method and apparatus, and an electronic device. The method comprises the steps of obtaining load information and resource information of a sub-machine in each physical machine; according to the load information and the resource information of the sub-machine in each physical machine, screening out the physical machine with the migratable sub-machine as a source end mother machine, and taking the physical machine with the non-migratable sub-machine as a destination end mother machine; taking a sub-machine in each source end master machine as a to-be-migrated sub-machine in sequence, and according to the resource information of the to-be-migrated sub-machine, taking the target end master machine of which the idle resource is greater than or equal to the resource information of the to-be-migrated sub-machine in each target end master machine and which belongs to the fragment resource as a to-be-migrated master machine corresponding to the to-be-migrated sub-machine; and thermally migrating each to-be-migrated sub-machine in each source end mother machine to the corresponding to-be-migrated mother machine. According to the technical scheme provided by the invention, scheduling management is carried out on the resources, fragment resources are filled, and a large-specification resource space is integrated, so that the utilization rate of resource management is improved.

The invention is suitable for the technical field of system fault monitoring, and provides a system fault monitoring method based on log real-time synchronization and active scanning, and the method comprises the steps: capturing a log increment in real time through a file system event monitoring trigger, analyzing a newly added log content, and synchronizing the log increment and the newly added log content to message middleware; a distributed active scanning unit is configured, the scanning period is dynamically adjusted, and node resources are expanded and shrunk based on the containerization technology; integrating the abnormal data of the user side, the interface state data of the server side and the log stream data, and constructing a multi-modal data set; a normalized error report increment is calculated based on a 30-minute sliding window, and a pre-processing instruction is generated in combination with a random forest algorithm and Bayesian optimization; executing corresponding operation according to the pre-processing instruction; according to the method, a traditional passive mode is broken through, the fault monitoring confidence coefficient is remarkably higher than that of an industry standard, sub-health perception and active blocking of a high-concurrency scene are supported, and a real-time and accurate intelligent operation and maintenance solution is provided for a complex system.

The invention discloses a data resource integration system based on cloud computing, and relates to the technical field of data resource processing, cloud platform data resources are preprocessed through a sequence generation module, timestamps are extracted, standardized time sequences are generated, and t-SNE is used for dimension reduction; the network construction module constructs a similarity complex network according to the sequence after dimension reduction, calculates the similarity of data points and constructs a simplified complex; the persistent calculation module calculates topological features of the simplified complex at different scales through a persistent coherence algorithm to generate a persistent coherence graph; the drift identification module compares the persistent homology graphs in different time periods, calculates a Wasserstein distance and identifies data drift; and the resource integration module classifies and screens data resources according to the drifting result, retains valid data, eliminates invalid data and realizes dynamic integration. And comparing and analyzing the persistent homograph, intelligently classifying and screening data resources based on a drift identification result, automatically eliminating invalid or noise data, and retaining data resources valuable for analysis.

The invention relates to a large casting and forging material research and development system, belongs to the technical field of material research and development, and solves the problems of low research and development efficiency, high cost, poor resource integration capability and difficulty in data collection during material research and development of large castings and forgings in the prior art. The system comprises an intelligent data center which is used for data acquisition, data storage and data model construction and updating in each large casting and forging material research and development project; the integrated calculation center is used for constructing and updating a simulation calculation model in each large casting and forging material research and development project; the research and development workbench is used for constructing a project research and development path of the large-scale casting and forging material research and development project, performing research and development operation based on the project research and development path and collecting and auditing research and development data; wherein the data model of the intelligent data center and the simulation calculation model of the integrated calculation center are adopted in the research and development operation process. The research and development efficiency of large casting and forging materials is improved, and data is efficiently collected.

The invention relates to the technical field of resource integration data analysis, in particular to a high-precision map testing method and system based on resource integration, and the method comprises the steps: improving the accuracy and reliability of a map through integrating and collecting map information of a plurality of data sources; map information of different data sources is preprocessed, so that the accuracy and reliability of data analysis are ensured; the authority and credibility of the digital map are improved through precision evaluation analysis, weight distribution and model construction; by testing and analyzing the response rate and the deviation degree, the actual performance of the digital map can be evaluated, and by generating a high-precision map test report, a technician is helped to understand a data analysis result and rapidly take adjustment and optimization measures. The method and the device are used for solving the technical problem that the digital map is not accurately tested and intelligently adjusted and optimized in the existing scheme.

The invention discloses a resource integration platform and a resource optimization configuration method for kindergarten integrated service, and relates to the field. According to the resource optimization configuration method for the kindergarten integrated service, resource service data of each functional area of the kindergarten integrated service to be optimized is obtained and comprehensively analyzed, and a resource pressure bearing index of each functional area is obtained; obtaining infant individual care characteristic data of each functional area, performing data analysis to obtain a comprehensive care demand index of each functional area, and performing comprehensive analysis in combination with the resource pressure bearing index to obtain a care resource regulation index of each functional area; according to the invention, the preset resource optimization configuration measures are taken for each functional area based on the nursing resource regulation and control index, so that the actual nursing load state of the functional area can be accurately reflected, and manpower tension and space waste caused by the fact that the actual nursing demand of the infant is not matched are avoided; and therefore, the nursing matching accuracy and the overall resource adaptation level can be improved.

The invention relates to the technical field of artificial intelligence large models, and particularly provides a method and device for realizing automatic examination and approval of informatization projects, and the method comprises the following steps: S1, integrating computing power resources and constructing a computing platform; s2, integrating the government affair knowledge base and historical project data; s3, training and optimizing a Hainot big model; s4, constructing an examination and approval model and realizing functions; and S5, testing and optimizing. Compared with the prior art, the whole efficiency of informatization construction can be improved, more accurate and efficient services can be provided for departments, and work is promoted to develop in the more intelligent and convenient direction.

The invention discloses an interactive programming data generation method which is used for the technical field of large language models. The method comprises the following steps: receiving a user demand carrying a target code unit, and obtaining a directed graph formed by resource information, the code unit and a dependency relationship thereof in a running environment; screening related resources based on demands to generate a target resource integration text used for describing resource application conditions, and extracting a sub-graph associated with a target code unit through a directed graph to generate a target code segment; combining historical dialogues, resource integration texts and code segment arrangement cues, and guiding the large language model to output prediction information including codes, interpretations or tool calling instructions. According to the method, by dynamically integrating the environment context and the code dependency relationship, it is ensured that the generated code is matched with the actual working space of the user, multi-round dialogue iterative optimization is supported, and the accuracy and practicability of the intelligent agent in interactive programming are improved.