2748 results about "Resource information" patented technology

The invention belongs to the technical field of fishery resource informatization management and ecological prediction, and particularly relates to a dynamic prediction method of a resource habitat dynamic prediction system based on multi-source heterogeneous data fusion, and the method comprises the steps: a multi-source heterogeneous data collaborative collection and standardization processing module synchronously collects cross-regional data, and generates a time-space aligned standardized data set; the multi-modal habitat adaptability evaluation and prediction model is used for receiving the standardized data set as input, constructing environmental, biological and social modalities based on multi-source fusion data, and generating a habitat adaptability prediction result; the three-dimensional dynamic visualization and decision support platform is used for receiving the habitat suitability prediction result and generating a habitat thermodynamic diagram, a resource abundance gradient and an environmental parameter dynamic visualization display and decision under multiple spatial and temporal scales; according to the method, an international data collaboration mechanism, an ecological niche model optimization algorithm and a lightweight visualization engine are subjected to system-level integration, and an engineering solution is provided for biological resource protection of a sea area.

The present invention relates to the field of resource scheduling, and in particular to a resource scheduling method and apparatus based on computing cluster twin modeling. The method mainly comprises: constructing a time sequence matrix on the basis of available resource information of a computing cluster, and constructing a digital twin model for resource state on the basis of the time sequence matrix; extracting a time sequence from the digital twin model for resource state to construct a state space, solving a probability transfer matrix of the state space, and constructing a digital twin model for resource probability transfer on the basis of the probability transfer matrix; for the resource requirements, constructing a feasible solution set for resource allocation on the basis of the digital twin model for resource probability transfer, obtaining the feasible solution set for resource allocation on the basis of the feasible solution set, and forming a scheduling policy library on the basis of the feasible solution set; and receiving resource requests in parallel, extracting a corresponding feasible solution from the scheduling policy library, and allocating resources to each resource request by taking the feasible solution as an allocation policy. The present invention can realize unified quantitative description of real-time states of various resources, and can also realize dynamic intelligent optimal scheduling of the resources.

A process scheduling method and apparatus, a device and a storage medium, relating to the technical field of production scheduling. The method comprises: receiving a process scheduling request (S201); in response to the process scheduling request, determining an instruction being executed, an instruction to be executed, and scheduling resource information (S202); establishing an objective function and a constraint condition on the basis of the instruction being executed, the instruction to be executed, the scheduling resource information and corresponding actual scheduling information (S203); optimizing the objective function under the constraint condition by means of a swarm intelligence optimization algorithm to obtain an optimal solution of the objective function (S204); and using the optimal solution to determine an instruction execution policy, so as to perform process scheduling (S205). According to the process scheduling method, scheduling resources such as instruments and devices can be fully utilized, and the continuously growing demand for production scheduling is met.

The embodiment of the invention provides a task scheduling method, system and device based on cloud computing and a storage medium, and belongs to the technical field of computers. According to the method, task priorities are initialized according to task resource requirements, task types and task timeliness requirements, task scheduling strategies are determined according to task metadata and computing resource information, the task metadata comprise the task priorities and the task resource requirements, and the task scheduling strategies are used for representing task scheduling sequences and task allocation objects; task scheduling is carried out according to the task scheduling strategy, load fluctuation information and task demand change information of each computing node are obtained, the task scheduling strategy is adjusted according to the load fluctuation information and the task demand change information, and the step of task scheduling according to the task scheduling strategy is repeatedly executed, so that the task scheduling efficiency is improved in the task execution process. Task scheduling and allocation are adaptively adjusted based on load changes and task demand changes, and the task execution efficiency and the system resource utilization rate are improved.

Disclosed is a system and a method for blockchain-based virtual power plant management, relating to the field of virtual power plants. The system mainly includes three modules: an event-driven based demand rapid response and flexible resource optimal allocation module configured to adjust power supply and demand in real time according to power demand information and available resource information of an industrial park; a blockchain-based data collaboration module configured to establish a data sharing and transaction environment among various participants of a virtual power plant; and a visualization management module configured to provide a user interface for displaying, in real time, operational status and power transactions of the virtual power plant in the user interface. The system can effectively manage virtual power plants, enhancing the efficiency and transparency of power supply.

The invention relates to the technical field of software defined networks, in particular to an SRv6-based SDN (Software Defined Network) data center load balancing method and system, and the method comprises the steps: obtaining node real-time load and SRv6 routing available resource information through an SDN controller, constructing a load balancing model, and recognizing an overload node and a congestion link; generating a flow scheduling path in combination with segmented routing characteristics; in combination with network state information, monitoring the load condition in real time when the node is unbalanced or the link packet loss rate is gt; when 5%, a load balancing adjustment mechanism is triggered, unbalance types are distinguished, a related flow set is determined, optimization is performed by using a DQN algorithm, and a flow scheduling strategy is generated and dynamically adjusted by taking throughput maximization and unbalance degree minimization as targets; and issuing the scheduling strategy to the data center network equipment, updating the flow table through the Netconf protocol, and redistributing the flow. Therefore, the problems of low load balancing efficiency, insufficient dynamic adaptability, low resource utilization rate and the like in the prior art are solved.

The invention provides an intelligent computing power scheduling method in a distributed computing environment, and relates to the technical field of distributed computing, and the intelligent scheduling method specifically comprises the steps of collecting resource information data, constructing a resource and task model, evaluating a computing power demand, formulating an adjustment strategy, distributing and scheduling the computing power, monitoring and adjusting in real time, and feeding back and optimizing. Through comprehensive modeling and dynamic computing power evaluation of computing nodes and tasks and in combination with multiple intelligent scheduling strategies, accurate allocation of computing power resources can be realized, the problems of resource waste and node load imbalance are effectively avoided, the overall utilization rate of resources in a distributed computing environment is remarkably improved, and the computing power of the distributed computing environment is improved according to the characteristics and requirements of the tasks. The execution nodes are reasonably selected, the resource allocation is optimized, the task correlation is considered, the data transmission overhead is reduced, the task execution speed can be increased, the task completion time can be shortened, and the processing capacity and response speed of the system are improved.

The invention provides a calculation task scheduling method and system applied to a numerical control machining system, and the method comprises the steps: obtaining a task request set containing a machining process type and a real-time constraint identifier, extracting a processing complexity feature and a resource demand feature, calling a task priority evaluation model to generate a priority sequence, and carrying out the calculation of a task. Generating an initial scheduling scheme according to the sequence and current available resource information of the system, adaptively adjusting the initial scheduling scheme based on task execution state information fed back in real time, and generating a final scheduling scheme including a task execution sequence and resource allocation parameters, thereby comprehensively considering task complexity, resource requirements and real-time constraints, and improving the scheduling efficiency. Dynamic matching and optimal scheduling of tasks and resources are achieved, and the machining efficiency and the resource utilization rate of the numerical control machining system are effectively improved.

The invention relates to task scheduling, in particular to an intelligent task optimization method based on a self-adaptive multi-dimensional dependency graph and dynamic scheduling, which comprises the following steps of: inputting task information and resource information; constructing a self-adaptive multi-dimensional dependency graph according to the task information; in the task execution process, the task state of each task is monitored in real time, and the self-adaptive multi-dimensional dependency graph is dynamically updated; calculating the effective priority of the current task, and updating the effective priority of the current task according to the updated adaptive multi-dimensional dependency graph; dynamically scheduling the current task by adopting a dynamic scheduling algorithm; monitoring a task execution state and a resource use condition of each task in real time; according to the task execution state and the resource use condition, whether dynamic scheduling needs to be adjusted or not is judged until all tasks are executed; according to the technical scheme, the defect that in the prior art, accurate and efficient task optimization scheduling is difficult to carry out under the multi-dimensional dependency relationship between the tasks can be effectively overcome.

The invention provides an aerial remote sensing multi-platform collaborative observation task planning and optimization method and system, and relates to the technical field of collaborative planning, and the method comprises the steps: receiving the observation demand information of a target region and the resource information of a plurality of aerial remote sensing platforms, building a multi-platform collaborative observation task model based on a deep reinforcement learning algorithm, generating an initial task planning scheme by utilizing the constructed multi-platform collaborative observation task model, optimizing the initial task planning scheme by adopting an improved multi-target particle swarm optimization algorithm, and distributing corresponding specific execution instructions to each aerial remote sensing platform through a security encrypted communication link; state information, including positions, postures, energy levels and equipment working states, fed back by the aerial remote sensing platforms is received and processed in real time, and a current task planning scheme is dynamically adjusted according to the real-time state information based on a federated learning framework by utilizing edge nodes and cloud cooperative computing capability, so that adaptive optimization of tasks is realized.

The invention discloses an enterprise management optimization method and system of big data enabling ERP, and relates to the technical field of enterprise management, the method comprises the following steps: creating an enterprise research and development task, obtaining research and development resource information, and constructing a task-resource-technology associated dynamic knowledge graph; the task priority is adjusted based on a multi-task game, and a task-resource-technology task line is reconstructed; carrying out adaptive granularity disassembly on the reconstructed task line, generating task key points and establishing parallel sub-task lines; a task line is further optimized, a cross-department cooperation scheme is formulated, and enterprise management optimization is promoted. Therefore, real-time perception, dynamic priority response and efficient resource allocation are achieved, and the cross-department cooperation level is improved.

The invention discloses a low-orbit satellite beam scheduling and resource allocation method and device, equipment and a storage medium, and relates to the technical field of communication, and the method comprises the steps: determining the time slot priority of a to-be-scheduled user; dividing to-be-scheduled users into different user groups, and determining a wave potential interference matrix between the user groups; time slot allocation is carried out based on the time slot priorities of the to-be-scheduled users and the beam position interference matrix between the user groups, and a hopping beam spectrum is generated; determining a target to-be-scheduled user corresponding to an available beam in each time slot in an updating period of the hopping beam atlas; and performing resource allocation on each time slot based on the service priority of the target to-be-scheduled user of the available beam in each time slot. Through the above mode, interference isolation and time slot allocation calculation are completed based on user priorities and user grouping, multi-user resource allocation scheduling is carried out under a single time slot, air interface resource information is fully utilized, and the spectrum utilization rate and the system throughput rate are improved to the maximum degree.

A dynamic micro-service migration and route redirection method based on topology awareness comprises the following steps: S1, acquiring cloud computing data center and edge server resource information, an application request stream set and an initialized mirror image deployment matrix in a dynamic scene, and dividing time slot-based input request stream features; s2, in each time slot, a multi-mirror-image preliminary deployment scheme of the micro-service is obtained in a marginal gain optimal mode, and a node routing scheme is redirected according to a mirror image deployment matrix of a physical server node; and S3, according to the resource cost, the multi-mirror-image preliminary deployment scheme and the node routing scheme, screening out topology dependence pairs with abnormal bandwidths in a request stream, and based on topology perception, executing migration operation for micro-service mirror images on server nodes of links with abnormal bandwidths to obtain an optimal joint decision of micro-service deployment and request routing. According to the method, topology-aware micro-service mirror image migration and online redirection probability routing are realized in a dynamic cloud computing data center scene.

The invention discloses a network topology and equipment resource information display method and device, equipment, a medium and a product, and relates to the field of network topology monitoring, and the method comprises the steps: employing a data collection module to collect network data; the network data comprises network topology information and resource information; carrying out optimization processing on the network data by utilizing a data processing module, and determining optimized network data; dynamically generating a network topology view according to the optimized network data by using a generation module of a display screen interface on the basis of the equipment operation state and the site connection relationship; based on a user interaction module, the link information of each link, the basic information of each network node and the resource state information in the network topology are displayed in a graded mode according to the network topology view, the information display effect in a large-scale network environment is optimized, and the resource state can be displayed in real time.

The invention discloses an intelligent storage multi-AGV scheduling method and system, and relates to the technical field of storage scheduling. A graph optimization algorithm is combined with a multi-target scheduling tradeoff factor model to solve a task path, output a task allocation and path planning scheme, load the task allocation and path planning scheme to a time axis, and perform time sequence simulation on the path; potential congestion points or resource conflict areas in the paths are identified, part of AGV paths are optimized according to identification results, time passing pressure indexes and actual AGV operation states are used as input, scheduling verification is performed through multi-round offline simulation, a multi-AGV scheduling holographic data set is integrated, and a task state driven multi-AGV closed-loop scheduling scheme is constructed. According to the scheduling method, a multi-dimensional scheduling model fusing tasks, paths and resource information is constructed, and graph optimization, dynamic simulation and closed-loop control are combined, so that efficient collaboration and real-time response of multiple AGV systems are realized.

The embodiment of the invention provides a cloud side-end cooperative control method and system based on remote communication. The method comprises the following steps: generating a task scheduling input data set according to real-time operation state data generated by terminal equipment in a remote communication process, current computing resource information of an edge computing node and global optimization target parameters issued by a cloud management system; calling a preset task scheduling decision model to carry out structured splitting on the cloud side-end cooperative control task to obtain original task structure information; task structure labeling information is generated for sub-task labeling priorities in combination with multi-aspect information, and cyclic matching deduction processing is performed on the task structure labeling information to obtain a hierarchical resource allocation result; and distributing the sub-tasks to corresponding cloud edge end nodes (including a first node corresponding to the terminal device, a second node corresponding to the edge computing node and a third node corresponding to the cloud management system) for execution, and configuring a task execution process adjustment mechanism based on real-time resource change to adapt to resource fluctuation.

The invention provides an intelligent assignment scheduling method and system for transportation, and relates to the field of logistics management, and the method comprises the steps: obtaining order information and transport capacity resource information, the order information comprises cargo attributes, the own transport capacity resource information comprises vehicle shipping space parameters, and the external transport capacity resource information comprises simplified load parameters; and processing the internal image of the vehicle cabin by using a wavelet transform method so as to determine three-dimensional parameters of the abnormal area and generate accurate vehicle cabin space parameters. Based on the order information and the accurate vehicle shipping space parameters, vehicle and route allocation is carried out through a multi-target scheduling algorithm, and candidate scheduling schemes are generated; and executing three-dimensional loading feasibility verification by utilizing a multi-grid construction module so as to predict the deformation condition of the goods and the vehicle shipping space and mark the ghost volume. And finally outputting an optimal scheduling scheme in response to verification passing. According to the method, the refinement level of logistics transportation management is effectively improved, space conflicts and overload risks are avoided, and the transport capacity resource utilization efficiency and the transportation safety are improved.

The invention relates to the technical field of resource optimal configuration, in particular to an urban resource optimal configuration method and system based on multi-objective collaboration. The method comprises the following steps: preprocessing acquired urban resource related data; constructing a dynamic space-time knowledge graph based on the preprocessed urban resource related data; constructing a reinforcement learning-neural network hybrid optimization model, and taking the dynamic space-time knowledge graph as embedded representation of the model; performing multi-target training optimization on the model by adopting quantum calculation and a classical optimization algorithm; and generating a resource configuration decision based on the optimized model. The urban resource information can be more comprehensively and accurately acquired by constructing a multi-source data acquisition network, integrating space-time, structuralization, text, image and other multi-modal heterogeneous data, and performing feature extraction and fusion by using a space-time diagram convolutional network, a BERT model, an improved ResNet model and the like.

The invention belongs to the technical field of task allocation and progress tracking, and particularly discloses and provides a detection task allocation and progress tracking method based on a big data management system, which comprises the following steps: firstly, calculating the matching degree of task and processing end multi-aspect information through a weighted fusion algorithm, and selecting a processing end with the highest matching degree as a main node; judging whether a constraint processing task is met or not, collecting related data during task execution, performing coupling analysis to obtain a progress standard-reaching rate, marking the processing end of which the standard-reaching rate is smaller than a set value as abnormal, and calculating a secondary allocation abnormal processing end task through a matching degree in combination with the progress and residual resource information of the processing end; according to the method, the parameters of the to-be-detected task and the processing end are synthesized through the weighted fusion algorithm, the matching degree including calculation, storage, time matching degree and task type correlation degree is analyzed, global optimal matching of the task and the processing end in the aspects of computing power, storage, task types and adaptability is accurately evaluated, and the problem that the task and the processing end are not matched is solved.

The invention relates to an intelligent robot feeding and discharging method and system, and the method comprises the steps: obtaining the image information of a target material, carrying out the recognition and analysis of the image information of the target material, and obtaining an analysis result which comprises the position information and posture information of the target material; inputting the analysis result into a pre-trained operation decision model for analysis, and generating an action planning instruction corresponding to the target material; task scheduling operation is executed based on the current task state and the resource information, a task scheduling result is obtained, and the scheduling operation comprises task distribution optimization and path generation control; according to the task scheduling result and the action planning instruction, the target robot is scheduled to complete feeding and discharging operation; and in the loading and unloading execution process, the operation state is monitored in real time, the operation state is detected and analyzed, an interference detection result is obtained, and the action planning instruction is dynamically adjusted based on the interference detection result. The production line has the effect of improving the efficiency of the production line.

The invention discloses a power emergency resource allocation method and system based on a knowledge graph and an improved graph neural network. The method comprises the following steps: firstly, extracting key entities and relationships among the entities from multi-source data, and constructing a power emergency resource allocation knowledge graph; mapping the constructed knowledge graph to a vector space, and generating a vectorization representation for each entity and relationship in the knowledge graph; then, a dynamic relation weight mechanism is introduced into a classic relation graph convolutional network model, an improved relation graph convolutional network model is constructed, vectorized data of the knowledge graph is used as input, and the deep relation between entities is learned; when a new power emergency event occurs, the method comprises the following steps: firstly, updating information of the new emergency event in the knowledge graph, expanding fault equipment, influence range and emergency resource information, ensuring that the latest environment information is fully utilized, then performing knowledge reasoning by using a relational graph convolutional network model, and generating an optimal resource allocation scheme. Therefore, efficient and accurate emergency response is realized.

The invention discloses a human resource information management system and method based on big data, and relates to the technical field of human resources, and the system comprises a project demand analysis module which is used for extracting a skill list required by new project post personnel, determining task complexity, and generating skill parameter requirements; the employee screening module is used for obtaining the basic information of all the employees in service and dividing the employees in service into two types of crowds, one type is the crowd meeting the new project employment, and the other type is the crowd not meeting the new project employment, through big data analysis and multi-dimensional evaluation, accurate matching of the project demand and the employee ability is achieved, and the work efficiency is improved. Project execution quality and efficiency are improved, enterprise internal human resource potential is fully excavated, personnel can be reasonably allocated, and waste and idleness of human resources can be avoided.

The invention provides a computing power resource management and distribution method and system and a medium. The method comprises the following steps: integrating various computing power resources in a private network, establishing a computing power resource information base, analyzing a computing power resource request, establishing a service demand model, optimizing and matching a computing power resource configuration template, obtaining a computing power adaptation template, and establishing a service demand model; optimal computing power resource adaptation is carried out according to dynamic information of target computing power resources matched with the computing power adaptation template, isolation processing is carried out at the same time, monitored performance index data are processed, a load trend is predicted, a corresponding computing power resource adjustment strategy is obtained, and the computing power resources are optimized and expanded according to big data analysis; therefore, efficient utilization and elastic expansion of the computing power resources of the private network are realized by constructing the dynamic computing power resource pool and the resource scheduling model and monitoring, adjusting and optimizing an expansion mechanism, and the processing efficiency and security of big data services are remarkably improved.

The invention discloses a heterogeneous resource scheduling method and device of a cloud data center, a medium and a product, and relates to the technical field of cloud computing, and the method comprises the following steps: receiving a scheduling task of an application instance containing source architecture resource information; screening the processing nodes according to the computing power demand of the application instance and the specification reference values of the processing nodes, and determining candidate nodes according to the screening result; constructing an application performance portrait of the application instance according to the performance data of the application instance; according to the application performance portrait and the scheduling task, equivalent resource configuration corresponding to deployment of the application instance on the candidate node is determined, a score is determined based on the equivalent resource configuration and the specification reference value, and a target node is determined based on the score; and modifying the resource request of the application instance based on the equivalent resource configuration to obtain a modified resource request, and deploying the application instance to the target node, so that the target node performs resource configuration based on the modified resource request to complete heterogeneous resource scheduling. And equivalence quantization and intelligent scheduling of heterogeneous resources are realized.

The invention belongs to the technical field of cooperative control, and particularly relates to a development experiment box multi-device cooperative control method based on an open-source gap system. According to the method, access, identity authentication, automatic discovery and dynamic connection of experimental box equipment are realized through a distributed equipment management platform of an open-source Gao Mongolia system; a uniform resource scheduling mechanism is utilized to complete resource information collection, strategy matching and dynamic allocation; task allocation is carried out by adopting an intelligent task scheduling algorithm and combining equipment states and task requirements; real-time data transmission between devices is realized by means of a low-delay communication protocol; and finally, automatically summarizing and analyzing experimental data and generating a report. According to the method, the problems of low equipment identification efficiency, low resource utilization rate, low task allocation efficiency and the like in the existing development experiment box are solved, the intelligent level and the system stability of equipment cooperative control are improved, and the method is suitable for a multi-equipment cooperative control scene of the development experiment box.

The invention relates to a cross-region computing power cooperation system and method. The method comprises the following steps: acquiring computing power resource information of computing power cluster nodes of each region in a global range; obtaining a cluster pairing strategy according to the computing power resource information of the computing power cluster nodes of each region in the global range, and obtaining a logical computing power cluster group according to the cluster pairing strategy; computing power cluster nodes in the computing power cluster group are used for mutual cooperative computing; acquiring a task processing request, wherein information in the task processing request comprises a service attribute, a computing power demand and a data volume; and according to the task processing request and the computing power resource information of the computing power cluster nodes in the computing power cluster group, determining a target computing power cluster node matched with the service attribute, the computing power demand and the data volume from the computing power cluster group, and sending the task processing request to the target computing power cluster node. The method can improve the utilization rate of global computing power resources.

The invention provides a computer big data storage control system, and relates to the technical field of computers, and the system comprises a data evaluation module which is used for comprehensively calculating the real-time popularity of data, and the real-time popularity is obtained through the comprehensive calculation of the data access frequency and the access duration; performing quantitative evaluation on the real-time popularity of the data in combination with a multi-dimensional factor to obtain a value evaluation value of the data; analyzing the system resource information of the storage device on the computer to obtain a resource analysis result; and the intelligent contract execution module is used for carrying out comprehensive analysis and judgment on the real-time popularity, the value evaluation value and the system resource analysis result of the data so as to trigger adjustment of a storage strategy. According to the method, the real-time popularity model is constructed, the analytic hierarchy process is combined to achieve multi-dimensional value evaluation, the intelligent contract dynamically adjusts the storage strategy according to the evaluation result and the resource state, the data value and the storage medium are accurately matched, the problems that a traditional system is extensive in evaluation and rigid in strategy are solved, and efficient and accurate scheduling of storage resources is achieved.

The invention relates to a TSN network configuration automatic generation method and system based on an anti-Agent. The system is composed of a network state perception and intention reflection module, a YANG model configuration parameter and structure decision module and a YANG model intelligent generation module. The network state sensing and intention reflecting module is responsible for structurally sensing the network state and resource information, the YANG model configuration parameter and structure decision module fuses multi-source knowledge and context information, intelligently generates deployable TSN scheduling parameters and a YANG configuration structure for service requirements, and sends the TSN scheduling parameters and the YANG configuration structure to the network state sensing and intention reflecting module. And the YANG model intelligent generation module automatically generates a corresponding YANG configuration model according to an optimized result, and issues the YANG configuration model to target equipment after completing grammar and semantic verification, so that automatic deployment of network configuration is realized. According to the method, the intellectualization level of TSN network configuration and the deployment automation capability are remarkably improved by introducing the reflection reasoning and feedback optimization technology.

The invention provides a dynamic access control management method and system based on a zero-trust architecture. The method comprises the following steps: acquiring original feature data, constructing feature data, distributing a label for the feature data, generating an identity chain based on the feature data and the label, performing encrypted signature and authentication on the identity chain, and acquiring an initial authentication result set; performing sample attack resistance on the initial authentication result set so as to increase the detection capability of the authentication result set and prevent fake identity attack, and generating a final authentication result set; obtaining the user identity credibility based on the authentication result set, obtaining resource information of the user request, dynamically adjusting the access permission of the user request based on the user identity credibility, and outputting the user access permission; and performing identity chain monitoring in real time, updating the access authority through an identity credibility updating model according to the dynamically calculated user identity credibility and the real-time access record, and triggering further security measures or authority adjustment through anomaly detection.

The embodiment of the invention relates to the field of large language models and multi-agents, and provides a multi-agent collaborative query response method based on a state dependency graph and a related device, and the method comprises the following steps: receiving a to-be-queried text input by a user; performing query semantic analysis on the to-be-queried text to obtain the analyzed to-be-queried text and query complexity; instructing the intention recognition agent to generate a domain index; calling a preference matching agent to generate preference information; calling a resource retrieval agent to query to obtain query use resource information corresponding to the to-be-queried text according to the query complexity; the query response information is generated at least according to the historical context of the to-be-queried text, the to-be-queried text, the analyzed to-be-queried text, the field structure information, the field index, the preference information and the query use resource information, and the personalized service corresponding to the to-be-queried text currently input by the user can be automatically adapted according to the actual interaction scene; and response type and high-efficiency intelligent assistance is realized.