308 results about "Task analysis" patented technology

The invention discloses a control method for a multi-modal information fusion intelligent robot with a body. The control method comprises the following steps: initializing a system, and collecting surrounding physical environment and object state information and a natural language instruction of a user; performing scene understanding and task analysis, processing data through a multi-modal information fusion mechanism and a cross-modal attention module, and generating unified multi-modal data; task planning and priority ranking are carried out, complex tasks are decomposed into subtask sequences, and a priority ranking layer dynamically adjusts the execution sequence; performing action execution and feedback adjustment, and generating a control instruction through a self-adaptive operation control algorithm; continuous learning and strategy verification are carried out, integrated execution is realized by using a hybrid AI system, and the robustness of an operation strategy is verified through a simulation environment; and closed-loop iteration is carried out to realize real-time response of the intelligent robot with the body. According to the method, the perception understanding precision and the task execution efficiency of the intelligent robot with the body are improved, the operation precision adaptability and the system robustness flexibility are guaranteed, and the method is suitable for multiple scenes.

The invention relates to the technical field of sharing management, in particular to an instrument sharing management method and system based on a cloud platform, and the method comprises the following steps: obtaining a task generation structure sequence, generating and publishing screening equipment, constructing a channel list generation partition table, and binding a resource output management record. According to the method, the experimental task text content is automatically extracted to perform refined analysis on the behavior structure relationship, the cooperative relationship between the devices is accurately split according to the operation sequence and the behavior difference, and the device response capability and the resource occupancy characteristic are quantitatively identified through the multi-dimensional index; the conflict relation is executed to jointly judge by difference analysis and resource intersection, a priority execution path is constructed in the equipment operation sorting and channel scheduling process according to a response level, and task blocks are accurately divided by jointly deducing an execution proportion and a starting interval in operation time interval arrangement; and automatic closed-loop linkage management of task analysis, equipment identification, conflict detection, priority ordering, block division, resource allocation and sharing registration is realized.

The invention belongs to the field of unmanned aerial vehicle control, and particularly relates to a complex task-oriented unmanned aerial vehicle modular integrated control platform, which comprises an acquisition module for acquiring and processing information such as a real-time flight state of an unmanned aerial vehicle group, a complex task text, meteorological data and the like; the task analysis module decomposes a complex task into a single subtask set and an incidence matrix, and evaluates the task complexity for resource allocation; the atmospheric analysis module is used for inverting a real-time flight association adjustment graph based on the corrected meteorological data, flight states and the like in combination with a target optimization algorithm and an optimization function taking resource quantity minimization and task completion probability maximization as targets; the record analysis module generates a performance transfer matrix by using a hidden Markov algorithm; and the flight control module responds to the adjustment graph to correct the flight state, and ensures that the task completion probability meets a preset safety threshold. According to the platform, efficient cooperative control and resource optimization allocation of the unmanned aerial vehicle group under complex tasks are realized through modular integration.

The invention relates to the technical field of computing power-energy consumption optimization, in particular to an edge AI reasoning computing power-power consumption cooperative system, which comprises a task decomposition module for decomposing a complete reasoning task into n independent sub-tasks, n being greater than or equal to 1; the task analysis module is used for extracting feature parameters of the subtasks and generating task feature vectors; and the computing power prediction module inputs the obtained sub-task feature vectors and different computing unit type labels into a pre-constructed task computing power demand prediction model, and predicts the computing power demands of the sub-tasks on different computing units. In the scheme construction and task allocation stage, the system carries out candidate calculation unit screening and combination generation based on a multi-scheme calculation power energy consumption matrix, calculates the total energy consumption and time delay matching degree of each combination, and selects an execution scheme with the lowest energy consumption under the real-time condition from the total energy consumption and time delay matching degree. The mechanism enables each subtask to be flexibly migrated between different types of computing units.

The invention relates to an owned task planning method and system based on search guided by a large language model. The method comprises the steps that in the preprocessing stage, a simulation module based on a large language model is used for generating a precondition function and an effect function corresponding to each action type according to environment meta-information; in a task analysis stage, a translation module based on a large language model is used for translating task description in a natural language form into a structured landing predicate set; in the exploration stage, an exploration module based on a large language model is used for guiding the intelligent agent to explore the environment until all objects related to tasks are observed; in the searching stage, a searching and screening module based on a large language model is used for executing a searching algorithm from the current state, an action space during searching is compressed, a complete task plan is generated, and the task plan is executed by an intelligent agent in the environment. According to the method, the interpretability and controllability of planning can be improved, so that the search process is feasible and efficient in a real task environment.

The invention discloses an agent construction and development system based on a knowledge graph, and the system comprises a data collection and ontology construction module which is used for generating basic graph data; the polymorphic knowledge cell construction module is used for generating a machine-readable rule and an evidence template; the semantic mapping and deduction module is used for determining forward and reverse mapping modes and setting a consistency threshold value and a violation degree weight; the task analysis and planning module is used for outputting a feasible path and generating a plan sequence and tool arrangement; the execution and correction module is used for generating an evidence packet, constructing a forward chain, carrying out reverse reconstruction by utilizing reverse mapping, generating anti-fact candidates and writing back or backing back the anti-fact candidates; and the version and audit module is used for generating a version fingerprint for the polymorphic knowledge cellular graph which is written back and providing query and audit functions. According to the method, adaptive planning, reliable execution and traceable auditing of the intelligent agent in a dynamic task are realized by constructing polymorphic knowledge cells and cross-layer semantic mapping.

The invention belongs to the technical field of crossing of artificial intelligence and computer system architecture, particularly relates to a computing power center equipment power supply intelligent system of a knowledge graph, and aims to solve the problem of low energy efficiency caused by extensive power supply management, unbalanced energy consumption and lagged load response. The system comprises a task analysis module, a dynamic load prediction module, a multi-source power supply scheduling module, an equipment-level power supply control module and a feedback optimization module, through load prediction and multi-source power supply cooperative scheduling driven by task semantics, voltage and current dynamic adjustment and sleep wake-up control are achieved, and a strategy is optimized online based on operation feedback. The scheme improves energy efficiency, equipment life and system stability.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an artificial intelligence data annotation and cue word automatic construction engine system, which comprises a data annotation module for firstly carrying out preliminary annotation on data based on a pre-training model, then automatically annotating a data sample through an active learning algorithm, and meanwhile, monitoring the quality of annotated data in real time; and the cue word automatic construction module generates cue words based on task analysis, optimizes the cue words by using a reinforcement learning technology, and performs classified storage and management on the generated cue words. By adopting the semi-automatic labeling and active learning labeling functions, not only can the workload be greatly reduced, but also the unnecessary labeling work can be reduced, so that the labeling efficiency is remarkably improved, the labeling time is shortened, the large-scale data labeling requirement is met, and the problem of efficiency bottleneck caused by slow manual labeling is solved.

The invention relates to the technical field of three-dimensional modeling, in particular to an on-orbit three-dimensional modeling method and system. The method comprises the following steps: mapping a task demand input by a user into a task specification object; carrying out mixed retrieval on the task template library, carrying out atlas consistency verification on the retrieved task templates, and sorting candidate templates after the verification is passed; expanding the high-score candidate template into an executable high-score operator sequence to serve as a task chain to be output; mapping each operator in the task chain into an MCP calling node to obtain an MCP calling chain; and calling a data processing tool and a three-dimensional modeling tool to generate a preliminary three-dimensional model, then performing incremental correction and local optimization through a dynamic feedback updating mechanism, and downloading the updated incremental three-dimensional data to a ground data center. Automatic task analysis is achieved, and manual intervention is not needed. The method has the lightweight modeling capability, knowledge-driven complementation can be realized, and a collaborative mode of on-orbit quick response and ground fine reconstruction is realized.

The invention provides a task analysis execution method and system based on a multi-modal large model, and belongs to the field of computers.The task analysis execution method comprises the steps that S1, multi-modal task data are received, a double-layer cross-modal fusion mechanism is adopted to conduct unified coding fusion on the multi-modal task data, and fusion features are obtained; s2, performing alignment processing on the fusion features based on a multi-modal embedding alignment mechanism to obtain alignment features, and performing structured task analysis based on the alignment features by using a pre-training language model to obtain task description; s3, decomposing the composite task into atomic tasks, and learning an optimal decomposition strategy by using an Actor-Critic network to form an atomic task set; and S4, generating a comprehensive adaptation score through a dynamic scheduling mechanism of collaborative awareness, and performing task distributed execution based on the comprehensive adaptation score. According to the method, the complex task is intelligently decomposed into the atomic task set capable of being executed in parallel for task distributed execution, and the task understanding accuracy and execution efficiency are improved.

The invention provides a medical data-oriented efficient privacy computing fusion engine method and system, and the method comprises the steps: constructing a medical data encryption and decryption system as a basis, and carrying out the standardized packaging of federated learning, homomorphic encryption, secure multi-party computing and other technologies, a unified technical framework comprising a data encryption interface, a ciphertext calculation interface and a result decryption interface is defined; an intelligent task analysis mechanism is adopted, high-level medical analysis requirements can be automatically decomposed into privacy calculation subtask sequences, and multi-technology collaboration is achieved through an execution engine; meanwhile, the functions of resource monitoring and performance analysis are achieved, technology switching and parameter adjustment and optimization during operation are supported, and a self-optimization execution environment is formed. And finally, displaying through a friendly application interface and visualization. According to the method, the problems of single technical route, low calculation efficiency, poor resource utilization rate, serious memory limitation and the like in medical data privacy protection are solved.

The invention relates to the technical field of path planning, in particular to a low-altitude airspace aircraft flight path planning system which comprises a task analysis module, an airspace modeling module, a path generation module, a feasibility verification module and a path output module. According to the method, sight distance flight rule grade judgment is introduced in airspace boundary extraction to eliminate airspace sections which do not meet the minimum sight distance reference requirement, a connection diagram is constructed, combined calculation of the wind shear angle and the building projection density is executed, and through quantitative analysis of the ratio of the path section height to the wind pressure change gradient, the wind shear angle and the building projection density are calculated. The method comprises the following steps: performing intensity risk labeling on a path in an unstable airflow area to realize path continuity and stability discrimination, constructing a continuous instruction point group based on a stable fragment sequence in a path combination stage, and setting speed and steering adjustment thresholds, so as to enhance the adaptability and execution robustness of a path execution sequence to flight control; and the air route feasibility and the risk avoidance capability in a complex low-altitude environment are improved.

The invention discloses an AI development optimization system based on dynamic learning and an adaptive algorithm, and relates to the technical field of artificial intelligence, and the system comprises a collection module which collects GPU occupation data and transmission channel state data, and generates a separation operation signal; the identification module is used for triggering operation task analysis according to the separation operation signal and distributing a transmission channel identifier for the subtask; the path module queries the idle state of each computing unit of the GPU and generates an exclusive communication path; the detection module is used for running the subtasks in the exclusive communication path and outputting channel signal mixing degree; the triggering module is used for setting a mixing degree threshold value and triggering a path replacement signal and a merging operation signal; and the merging module suspends the sub-task processing process based on the merging operation signal, performs merging according to the operation sequence of the sub-tasks, and outputs a complete task. According to the method, efficient resource scheduling and task optimization of AI development are realized through coordination of dynamic task topology analysis and self-adaptive communication path binding.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-agent cooperation report generation method, device, equipment and medium, and the method comprises the steps: obtaining a compliance requirement file and a task description text; analyzing the task description text through an intention recognition agent to generate a task intention recognition result; processing the compliance requirement file and the task intention recognition result through a key point extraction agent to generate compliance requirement key points; executing the compliance requirement key points through the data processing agent to generate data visual representation; and synthesizing compliance requirement key points and data visual representation through the report generation agent to generate report content. According to the method, task analysis, key point extraction, data processing and report synthesis links are sequentially connected and automatically executed in a multi-agent division cooperation mode, so that compliance files and task description can be quickly converted into structured key points, and business data can be efficiently processed and visually presented.

The invention discloses a table data analysis method fusing a large language model and a ReAct framework. The table data analysis method comprises the following steps: receiving and processing table data and natural language query input by a user through a file analysis module; performing task analysis and code generation in the ReAct framework by using an LLM decision engine; constructing a multi-stage verification module to perform three-stage verification of semantic calibration, code pre-check and logic verification on the generated code to enhance the code robustness and improve the result accuracy; a CNN-BiLSTM hybrid neural network model is adopted to construct a self-correction module, error classification is carried out on codes which fail to be verified, and a correction strategy is fed back; and executing the generated code, and returning a structured result after the ReAct control process is finished. According to the method, the problems that error propagation is not corrected, multi-Agent resource consumption is large and complex query accuracy is low in an existing table analysis technology are effectively solved, the accuracy, efficiency and reliability of data analysis are remarkably improved, and meanwhile computing resource consumption is greatly reduced.

The invention discloses a human metabolism multi-task analysis method based on a large language model, which comprises the following steps of: firstly, constructing a unified multi-task data set containing metabolic reaction prediction, and converting tasks such as compound expert description, enzyme classification, reaction type identification and product prediction into a standardized'question-thinking-answer 'text format; and then a Qwen2.5-7B large language model is adopted as an infrastructure, and supervised training is performed through a training parameter fine tuning technology, so that the model can infer the biochemical reaction process. Experimental results show that the method disclosed by the invention has relatively high efficiency and accuracy in a test with the same standard as those of Deepseekv3 and Qwen 2.5-7B.

The invention provides a dynamic hybrid retrieval method and system for multi-modal data in the aviation field, and relates to the technical field of data retrieval in the aviation field, and the method comprises the steps: S1, obtaining the aviation multi-modal data, and carrying out the cleaning and preprocessing of the multi-modal data; s2, performing multi-modal data knowledge base construction and dual representation by using aviation domain mode constraint; s3, performing knowledge graph global alignment and concept system enhancement of the multi-modal data; s4, performing task analysis to realize dynamic hybrid retrieval of the aviation multi-modal data; and S5, generating an aviation multi-modal data retrieval result to assist in carrying out aviation design. According to the method, unstructured data are uniformly converted into texts, and semantic alignment of cross-modal data is realized; an aviation domain knowledge dictionary or knowledge system is introduced to improve the credibility of the knowledge graph; a dynamic mixed retrieval mechanism is used, a'knowledge graph + vector database 'double-engine retrieval model is constructed, the processing capacity of a large language model is improved, and actual engineering requirements are met.

The invention discloses a medical business system processing method based on multi-agent cooperation, a medium and equipment, and belongs to the technical field of medical informatization. In the method, a task analysis agent receives a medical service request, calls a large language model to analyze a user intention, and generates a standardized task description; the routing scheduling agent decomposes the task and distributes the task to the corresponding service execution agent according to the task type and the service rule; the plurality of service execution agents process respective responsible subtasks in parallel, and call corresponding medical information system interfaces to complete specific service operation; and the result summarizing agent collects the processing results of the execution agents, and the processing results are integrated to generate a final service response. According to the invention, through specialized division and cooperation of multiple agents, automatic processing of complex medical services is realized, and the processing efficiency and the intelligent level of a medical information system can be significantly improved.

The invention is suitable for the technical field of computers, and provides a CPU and GPU cooperative work heterogeneous computing method and platform, and the method comprises the following steps: S1, carrying out the meta-task analysis of an input computing task, extracting the computing feature metadata of the computing task, and predicting the performance of the computing task based on a pre-trained performance prediction model; dynamically deciding execution path planning of the task between the CPU and the GPU; s2, according to the execution path planning, carrying out adaptive resource collaborative configuration; and S3, on the basis of the calculation feature metadata and the current hardware state, through a parameterized kernel template or a just-in-time compilation technology, heterogeneous perception optimized kernel codes are generated. The method effectively solves the problems that a task scheduling strategy is rigid, the bottleneck of memory and data transmission is prominent, calculation kernel optimization is insufficient and is lack of adaptability, and a system is lack of self-evolution and learning ability.

The invention belongs to the field of artificial intelligence and enterprise strategic technology prediction, and particularly relates to an enterprise technology evolution trend identification method and system based on large model reasoning. The system is composed of six modules: a data acquisition and preprocessing module unifies structured multi-source data; the enterprise technical portrait module constructs a research and development capability and technical trajectory map; the task analysis module utilizes a large model to understand natural language requirements and generates a multi-layer task structure; the knowledge modeling module is fused with the industry ontology to construct a semantic map; the trend identification module predicts a technical evolution path through time sequence modeling and multi-round reasoning; and the feedback optimization module forms a closed-loop learning mechanism based on the user behavior fine tuning model. According to the method, the semantic understanding and reasoning capabilities of a large model are fused, the knowledge graph, task analysis, trend modeling and a feedback mechanism are combined, the problems of insufficient structuralization, prediction lag and the like are effectively solved, the accuracy and foresight of enterprise technology trend recognition are improved, and intelligent support is provided for enterprise innovative deployment.

The invention relates to the technical field of data analysis, and particularly discloses an edge node-oriented artificial intelligence computing resource collaborative scheduling method and system, and the method comprises the steps: obtaining a target execution task, allocating tasks based on edge node resource collaborative scheduling capability, and analyzing scheduling process parameters to adjust allocation; monitoring task execution of the edge nodes, and evaluating the quality index set to optimize execution parameters; collecting and integrating execution results, and judging whether to give an early warning or not after comparison; the core represents the edge node scheduling capability by calculating a collaborative stability factor, dynamically adjusts task distribution, and optimizes abnormal edge node parameters based on a task execution quality evaluation index; dynamically adapting a scheduling strategy according to the dynamic nature of an edge environment; high-quality edge nodes are preferentially used, and resource overload is avoided; self-adaptive optimization is realized in combination with an artificial intelligence model, the overall performance and stability of the system are improved, and the method is suitable for complex edge scenes.

The invention particularly relates to a 3D visual modeling method based on the Internet, and relates to the technical field of distributed rendering, and the method comprises the steps: task analysis and intelligent splitting; dynamic resource evaluation and selection; distributing and scheduling dynamic tasks; and performing task execution and process monitoring. According to the method, the three-dimensional complexity evaluation model is constructed, geometry, material texture and illumination special effect complexity are quantified into calculated amount weight values, a unified scientific standard is provided for task splitting, and traditional fuzzy judgment depending on subjective experience is avoided; and a splitting mode is dynamically selected in combination with scene complexity variance: uniform splitting is carried out when the complexity difference is small, the tile size is adjusted according to local complexity when the difference is large, and long-tail tasks are eliminated from the source.

The invention provides a task agent generation and task processing method based on a meta agent, and relates to the technical field of autonomous agent application. The method mainly comprises the steps of initialization, task input, task analysis, subtask generation, task agent generation and distribution, task execution and cooperation, and result integration and feedback. According to the method, the meta-agent with task understanding, decomposition and planning capabilities is introduced, so that the task agent is dynamically generated according to task requirements, the tool calling process is optimized, the problem of low model processing efficiency caused by tool information overload in the prior art is effectively solved, and the model processing efficiency is improved. And the response speed, the flexibility and the intelligent level of task processing of the automatic software / hardware system are remarkably improved.

The invention provides a laser printer printing task intelligent optimization method and system, and relates to the technical field of data processing, and the method comprises the steps: receiving a first printing task queue of a laser printer, carrying out the task analysis, and determining the task characteristics of each printing task in the first printing task queue; collecting real-time state information of the laser printer, analyzing the real-time state information and task characteristics of the printing tasks, and determining priority labels of the printing tasks in the first printing task queue; printing task scheduling sorting is conducted on all the printing tasks in the first printing task queue according to the priority labels, a second printing task queue is obtained, and the printing tasks are sequentially executed based on the second printing task queue; and monitoring the printing state in real time in the printing execution process, and when the printing state changes, performing scheduling optimization on the second printing task queue again according to the multi-target optimization model, generating a third printing task queue and executing the third printing task queue until the printing task is completed.

The invention discloses a cloud and edge integrated industrial agent distributed task execution method and system. The method comprises the following steps: constructing a global agent at a cloud end, and deploying a local agent at an edge device; the global agent receives the production task and decomposes the production task into sub-tasks; sub-tasks are dynamically allocated based on the real-time state and capability of each edge device; the local agent executes the distributed subtasks and keeps state synchronization with the global agent; and establishing a cloud and edge collaborative learning mechanism. The system comprises a task analysis module, a resource sensing module, a dynamic allocation module, a collaborative execution module and an incremental learning module. According to the method, the problems of insufficient collaboration of cloud computing and edge computing and rigid task allocation in a traditional industrial system are solved, and intelligent collaboration of cloud edge resources and efficient execution of tasks are realized.

The invention discloses a wind power data cross-granularity processing method and system based on cloud edge collaboration, and relates to the technical field of wind power data processing, and the method comprises the steps: an edge node collects wind power equipment data, generates a multi-granularity feature vector set after synchronization and denoising processing, and uploads the multi-granularity feature vector set to a cloud; after receiving the multi-granularity feature vector set, the cloud constructs a cross-granularity trust map according to time association, spatial distribution and operation states, and transmits confidence among nodes to generate an embedded representation with a trusted weight; and performing weighted fusion on the feature vectors of different time scales and spatial positions by utilizing the embedded representation to generate an aggregation feature matrix and taking the aggregation feature matrix as task analysis input. According to the method, the cross-granularity trust atlas is generated based on weighted fusion of the time incidence matrix, the space incidence matrix and the operation state trust degree matrix at the cloud, and dynamic complementation and trust propagation among multi-dimensional spatial and temporal characteristics can be realized.

The invention discloses a multi-body system dynamics intelligent modeling calculation system and method based on a natural language, and belongs to the field of overall design of spacecrafts. The problems that in the prior art, when the simulation requirement related to extreme working conditions and strong nonlinear coupling is met, the period is long, manual experience is relied on, and the maintenance cost is high are solved. The system comprises a text segmentation embedding unit used for carrying out text segmentation embedding processing on a multi-body dynamics knowledge base and constructing a vectorized dynamics knowledge context; the large language model unit is used for receiving natural language task description input by a user, performing semantic understanding through LLM (Logistics Language Model) and generating a task analysis result according to dynamic knowledge context learning; and the task processing unit is used for classifying the tasks into inquiry tasks, command and control tasks or construction tasks according to the task analysis result, and calling corresponding modules according to LLM and MCP technologies. The method is used in the field of multi-body system dynamics modeling of spacecrafts.

The invention discloses a multi-delivery robot path collaborative optimization system and a scheduling method, and aims to solve the problems of non-uniform scheduling load, unsmooth path, weak conflict avoidance capability and poor dynamic adaptability of existing multi-robot scheduling. The system comprises a central scheduling module, a distribution robot terminal, an environment sensing module and a map management module, and realizes task analysis, state monitoring, path planning and conflict coordination. According to the method, region division and task balanced distribution are completed by improving a DARP algorithm, path coverage efficiency and smoothness are optimized by fusing a BA * algorithm, a cattle farming method and a TEB algorithm, multi-type path conflicts are solved by adopting a time window reservation and distributed negotiation strategy, and dynamic adjustment under new task access, robot faults and environment sudden change is supported. According to the method, the load balance, the path optimization effect and the conflict avoidance capability of multi-robot collaborative distribution can be remarkably improved, the efficient and stable execution of the distribution task is ensured, and the method is suitable for terminal distribution scenes such as communities and office buildings.

The invention discloses a GPU cluster task scheduling method and device, equipment and a medium. The method comprises the following steps: constructing a task dependency graph in response to a to-be-scheduled task; obtaining a matched scheduling strategy according to the task characteristics of the to-be-scheduled task, marking a node type for each computing node in the task dependency graph, and determining an available GPU resource pool according to the scheduling strategy and the node type; generating all candidate GPU allocation schemes, and calculating an end-to-end execution delay of each candidate GPU allocation scheme by using a preset delay calculation model; and according to each end-to-end execution time delay and the scheduling strategy, selecting a target GPU allocation scheme from all the candidate GPU allocation schemes, so that a cluster resource manager schedules a target GPU cluster to execute the to-be-scheduled task according to the target GPU allocation scheme. According to the method, a closed loop from task analysis, resource screening to scheme optimization is formed, and the accuracy, efficiency and adaptability of GPU cluster task scheduling are improved.

The invention discloses a development data query management method and system based on data security, and relates to the technical field of computers, the method comprises the following steps: obtaining a query task configured by a user at a client based on a target demand; performing access permission pre-verification from the dimensions of a user, a role and a data resource through a three-level permission model, and performing security processing on a query task and associated data after verification succeeds to generate a standardized query task with a security identifier; extracting multi-dimensional features of request execution time, task emergency degree, data scale and data type; generating a storage node priority sequence; a plurality of mainstream databases are adapted through a unified adaptation interface, and task analysis, conversion and execution are completed; monitoring a task execution state in real time; according to the method, access permission preprocessing can be carried out from multiple dimensions of users, roles and data resources through a three-level permission model and security processing, and the risks of unauthorized access and data leakage are avoided.