1548 results about "Data dynamics" patented technology

The invention relates to the technical field of cooperative control, in particular to a full-intelligent simulation load distributed cooperative control method, which comprises the following steps of: acquiring node load data, dynamically predicting, adjusting and distributing, performing cooperative scheduling optimization control, and generating an intelligent load control scheme. According to the method, the load state information is extracted in real time and standardized verification is carried out, so that the running state of each node has a unified measurement basis, task allocation is carried out in combination with node processing capacity and throughput performance, and task scheduling and node performance dynamic matching are realized; prejudgment type load regulation and control are achieved by combining historical data trend prediction with a current state, the risk of task delay and node overload is avoided, a task allocation strategy is continuously optimized by utilizing real-time state feedback, the resource use efficiency is improved, the cooperative relation between nodes is strengthened, and the sensitivity and consistency of system scheduling are guaranteed in a dynamic load change environment. And the full-process adaptivity and collaborative stability of task scheduling in a multi-node system are supported.

The invention discloses a computer memory bank fault prediction method and system based on deep learning, and relates to the technical field of computer hardware fault diagnosis, and the system comprises a multi-source time sequence data collection module which is used for obtaining memory bank operation state data in real time; the dynamic feature enhancement module is based on a composite architecture of a generative adversarial network and transfer learning, comprises a fault mode generator, and generates synthetic data consistent with real fault distribution by using an LSTM network; aligning feature spaces of different hardware platforms through a maximum mean difference loss function; the multi-modal fusion deep learning model comprises a space-time convolutional network, a graph attention network and an adaptive weight adjustment mechanism; and the fault early warning analysis module is used for analyzing a fault probability predicted value, an interpretable thermodynamic diagram and a maintenance suggestion. According to the invention, passive maintenance is changed into active prevention and control, and preposition and precision of fault management are realized through dual mechanisms of long-term trend prediction and short-term risk early warning.

The invention discloses a distributed time sequence library data management method supporting cold and hot data level-to-level management, and relates to the technical field of computer databases, comprising: receiving a write-in request of time sequence data, and executing preliminary write-in data aggregation and sorting; dynamically identifying data cold and hot attributes based on a multi-dimensional data cold and hot degree calculation model in combination with a write-in behavior and an access behavior of time series data; according to the cold and hot attribute recognition result, in combination with the hierarchy boundary of self-adaptive division, automatic hierarchical storage of the time series data is executed; based on a cold and hot data dynamic migration and scheduling mechanism, according to the access frequency and time decay characteristics of the time series data, dynamically adjusting the storage hierarchy of the time series data, and executing a data migration task; and constructing a hierarchical index system adaptive to different cold and hot attribute time series data, and combining query frequency dynamic identification and index structure automatic upgrading. By adopting a cold and hot data automatic identification and hierarchical storage mechanism, the overall performance and the resource utilization rate of the system are improved.

The invention discloses a shield muck volume estimation method and system based on image processing, and particularly relates to the technical field of tunnel engineering monitoring, and the method comprises the steps: obtaining muck RGB and depth images through an image collection device, generating high-precision three-dimensional point cloud data through the combination of laser scanning and a multispectral technology, and calculating the muck volume through a slicing method. And fusing the mass flow and water content data of the belt weigher, dynamically generating a calibration factor, and correcting a volume estimation result in real time. The system comprises an image acquisition module, a point cloud processing module, a volume calculation module and a dynamic calibration module, has the characteristics of automation, high precision, real-time feedback and the like, effectively solves the problems of large error and slow response of a traditional manual metering and single weighing mode, and is suitable for continuous and accurate monitoring of the volume of muck in the shield construction process.

The invention discloses a water conservancy facility full-period informatization management method and system based on digital twinning, and relates to the technical field of water conservancy informatization management, and the method comprises the steps: collecting water conservancy facility data, constructing a water conservancy facility digital twinning model, constructing an interactive structure body through employing three-dimensional geometric modeling, topological coding, material attribute and operation state fields, and constructing a water conservancy facility digital twinning model. Node mapping, attribute inheritance and time evolution rules are established; on-site progress, quality and deviation data are collected through a construction period sensing device, a state-driven mapping mechanism is designed, various data are dynamically mapped to a digital twin model of the water conservancy facility, and a structure-environment collaborative sensing mechanism is constructed in an operation stage; based on the digital twinborn model data of the water conservancy facility, a risk identification model is established, and the dam break risk, gate clamping stagnation, pump station failure and channel clogging are predicted. According to the method, linkage of water conservancy facility multi-source sensing, digital twin mapping and risk identification is realized, and the operation safety monitoring precision and the regulation and control efficiency are improved.

The invention relates to the technical field of health management systems, and particularly discloses a personalized AI intelligent health management system based on multi-source data fusion. Comprising a multi-modal health data dynamic acquisition and fusion module, a personalized dynamic health portrait construction module, a hierarchical risk early warning and root cause inference module, a self-adaptive personalized intervention strategy generation and closed-loop optimization module and a privacy protection and federated learning module which are connected in sequence. According to the system, standardized acquisition, quality verification and feature level fusion of different types of health data are realized through a multi-modal health data dynamic acquisition and fusion module, the problems of multi-source data dispersion and poor fusion effect in the prior art are solved, and a high-quality data basis is provided for subsequent health analysis; through an embedded dual adaptive calibration mechanism and a dynamic updating unit, portrait calibration is carried out in combination with a user individual historical baseline and a group generality mode, and the change of a user health state can be adapted in real time.

The invention relates to a large model business logic processing method and system based on workflow engine and domain knowledge fusion, and is suitable for automatic processing of complex multi-node and multi-branch business processes. According to the method, natural language input of a user is analyzed through a large language model, a service intention is recognized, and the service intention is converted into an executable task process through a workflow engine. And the workflow engine dynamically adjusts an execution path according to rules and data in the domain knowledge graph to realize efficient parallel processing of tasks. The system integrates a workflow engine, a domain knowledge graph and a large language model, supports real-time data processing, rule matching, conflict resolution and decision optimization, is suitable for the fields of water conservancy, medical treatment, finance and the like, automatically generates and executes a complex business process, and optimizes a decision process. The system improves the accuracy and execution efficiency of business decision through deep fusion of domain knowledge.

The invention discloses a medical diagnosis auxiliary method and system based on thinking chain visualization, and belongs to the technical field of medical auxiliary diagnosis, and the method specifically comprises the steps: obtaining the related data of a patient, generating an initial diagnosis thinking chain, detecting and recognizing a target sub-chain node needing to be updated based on the graph structure difference when new medical data is received, and updating the target sub-chain node according to the target sub-chain node. Performing local increment updating on the target sub-chain, visualizing the updated thinking chain, and dynamically updating difference detection parameters and optimizing the thinking chain according to the backtracking operation or annotation data of the reasoning node; according to the method, the calculation burden and the graphic rendering overhead are reduced, frequent jitter or breakage of the chain structure is avoided, the response efficiency and the structural stability of the system to multi-stage data input in a complex diagnosis and treatment process are remarkably improved, the transparency and the understandability of reasoning logic are enhanced, and intelligent auxiliary diagnosis better meets the actual clinical requirements.

The invention discloses a liquid cooling charging module health state prediction system, and relates to the technical field of electrochemical detection, and the system comprises a multi-parameter collection unit which collects the pressure and flow data of a cooling liquid, and is provided with an independent collection node at each branch of a double-gun charging system, and provides basic data for monitoring; the dynamic calibration unit communicates with the acquisition unit, compensates data drift based on a temperature-vibration interference model of machine learning training, executes baseline calibration by using an idle period to update a reference value, and guarantees data accuracy; the leakage detection unit is used for calculating a data change rate after compensation, outputting an early warning according to a preset condition and positioning a leakage branch; and the health state evaluation unit fuses the early warning signal and the historical operation data to generate a prediction result. The problems that tiny leakage detection is difficult and a sensor is prone to interference can be solved, manual inspection is reduced, the maintenance cost is reduced, and the requirement of a high-power charging scene is met.

The invention relates to the technical field of terrain reconstruction and path planning, and particularly provides a night terrain reconstruction and path planning method and system based on multi-sensor collaboration, and the method comprises the steps: obtaining the multi-source sensing data of a night environment through a data collection module; performing multi-source data fusion processing on the multi-source sensing data to generate a night environment reconstruction model fusing multi-dimensional features; performing obstacle semantic analysis on the night environment reconstruction model, extracting static obstacle distribution information and a dynamic obstacle movement track, and constructing a composite environment map; according to the composite environment map, a passable path is generated through a dynamic optimization algorithm in combination with terrain passability parameters and kinematics constraint conditions of a mobile carrier, and path parameters are dynamically adjusted based on real-time update data of a night environment reconstruction model; when it is detected that the obstacle distance or the motion state exceeds a safety threshold value, path real-time correction and graded obstacle avoidance instruction output are triggered through multi-sensor confidence evaluation and path collision risk prediction.

The invention provides a man-machine collaborative intelligent writing method and system for medical documents, and relates to the technical field of man-machine collaboration.The method comprises the steps that multi-modal clinical data of a patient is obtained for cross-modal semantic alignment, reasoning expansion is conducted based on a multi-level traditional Chinese medicine knowledge graph to generate a structured dialectical framework, and the structured dialectical framework is subjected to data processing; a logic consistency verification graph of a candidate medical record fragment set is constructed to solve semantic conflicts, and a medical record generation model is dynamically optimized in combination with clinical actual curative effect data, so that the intelligence degree, the standardization level and the clinical diagnosis and treatment value of traditional Chinese medicine medical record writing are improved.

The invention discloses a government affair data dynamic authorization management method based on secure multi-party computing, and relates to the field of government affair data security authorization management, and the method comprises the steps: distributing a unique identity identifier for each participant, generating an asymmetric key pair through employing a national secret SM2 algorithm, registering a public key and mechanism attribute information to an alliance chain smart contract, and generating an identity certificate package; and based on attribute information in the identity credential packet, the participants cooperatively generate anti-quantum dynamic attribute-based encryption key fragments through a secure multi-party computing protocol, and the anti-quantum dynamic attribute-based encryption key fragments are distributed to the participants by adopting a threshold secret sharing technology. Threshold decryption and token verification are achieved through an alliance chain verification intelligent contract, an authorization record is generated, an access control strategy of an edge computing node is configured according to the authorization record, an oblivious transmission protocol is adopted to respond to data query, and an audit node monitors an access log, dynamically adjusts attribute weight parameters and synchronizes the attribute weight parameters to a key fragment.

A system and method for predictive analysis, scenario simulation, and decision optimization is provided. The system includes a prediction management system executed on a distributed computing infrastructure, and a prediction engine configured to receive input data, including event parameters, user-defined constraints, real-time data feeds, and historical trends. The prediction engine generates predictive models using algorithms trained on historical event outcomes, assigns probability scores and confidence intervals to potential outcomes, and dynamically updates the models based on new input data. Actionable insights are generated and ranked according to predefined success criteria. A non-transitory computer-readable medium is used to store the predictive models, outcome probabilities, and actionable insights for subsequent analysis and reporting. This system facilitates enhanced decision-making by offering real-time insights and continuously refined predictions, thereby optimizing responses to complex events and scenarios.

The invention discloses a real-time back break control method of a heading machine based on visual association and digital twinning, which relates to the technical field of intelligent control of coal mining equipment, and comprises the following four steps: processing binocular visual data through a double-domain double-branch Transformer model, extracting coal rock and roadway structure characteristics and outputting cutting head offset; a GCN-LSTM model is adopted to fuse visual, laser radar and fiber-optic gyroscope data, and dynamic calibration and output of a three-dimensional pose are carried out; a roadway three-dimensional model is constructed based on the SLAM technology, and real-time updating of a digital twin model is achieved through edge-cloud collaboration; and in combination with a reinforcement learning strategy, generating a cutting parameter adjustment instruction according to the deviation of the digital twin model, and correcting model parameters through error feedback. The method solves the problems of low visual recognition precision, sensor data drift, digital twinning update lag and the like in an underground complex environment, improves the back break control precision, tunneling efficiency and equipment stability, and is suitable for tunneling operation under complex geological conditions.

The invention relates to the field of power networks, and provides an intelligent generation method for power equipment accident recovery. The method comprises the following steps: converting key semantic elements into a structured query instruction; acquiring operation state data of the power grid equipment from a power grid monitoring system in real time, and dynamically updating the operation state data to a pre-constructed power knowledge graph; on the basis of a structured query instruction, performing graph traversal and reasoning on the updated electric power knowledge graph, and searching all reachable electric paths from an available power supply point to a target recovery object; carrying out feasibility evaluation on the reachable electrical path in combination with an operation constraint condition, and generating a candidate recovery path; performing multi-objective optimization sorting on the candidate recovery paths to generate an operation instruction sequence; and converting the operation instruction sequence into at least one form of output scheme. According to the invention, automatic generation and optimization from the natural language instruction to the intelligent recovery scheme are realized.

The invention relates to the technical field of enterprise data management, in particular to a lightweight-based enterprise data dynamic integrated management system, which is characterized in that an acquisition module is used for deploying edge computing nodes, receiving multi-source heterogeneous information streams from manufacturing execution systems and equipment logs, dynamically analyzing and standardizing the information streams, and adding metadata tags; uploading is carried out in a batch processing mode; the map construction module is used for constructing a semiconductor blood relationship map by taking the standardized key information as a blood relationship clue; a graph database is used for efficient storage, and a RESTful API interface is configured to support batch import, so that data storage and relevance expression are more flexible and efficient; the prediction module performs reasoning on the atlas by adopting a graph neural network to generate predictive risk distribution, and a correlation analysis set generated by the prediction module is stored back to the atlas in a structured manner; by introducing a multi-thread concurrent write-in and lock mechanism, the atlas supports complex combination query based on a Cypher query language, and supports multi-level and traceability query.

The invention discloses a reinforcement learning model training method and device based on dynamic data selection and electronic equipment, and the method comprises the steps: carrying out the screening from an original mathematical problem data pool according to preset data quality, diversity and difficulty evaluation indexes, and obtaining a candidate training set; calculating a data validity score of each training sample in the candidate training set; difficulty filtering is carried out on the candidate training set, and an adaptive training course is constructed; further filtering the candidate training set according to the solution reasoning process and the reward score of the candidate training set; dynamically selecting a data subset for the current training period by setting a temperature parameter attenuated along with the training process and a probability threshold based on the further filtered candidate training set and the data validity score; the selected data subsets are put into a reinforcement learning model for training, and the reinforcement learning model is trained in an optimized mode according to the correctness reward and the format reward. The model training period can be remarkably shortened, and the performance equivalent to or even better than that of full data training is achieved.

The invention discloses an intelligent control system for a high-precision emulsifying pot, relates to the field of intelligent control, and is used for collecting multi-dimensional data such as temperature field distribution, material rheological characteristics, liquid drop behaviors and mechanical states in real time through distributed sensors and monitoring the emulsifying process in real time. The system evaluates the phase stability risk level by adopting a multi-source data fusion technology, and dynamically adjusts the process parameters such as the stirring rotating speed, the emulsifier flow and the heating power according to the risk level. For a high-risk state, by analyzing the correlation between a temperature field and torque fluctuation, an abnormal reason is accurately identified, and a targeted regulation and control measure is triggered. A liquid drop coalescence monitoring mechanism is introduced into the system, and when liquid drop coalescence exceeds the standard, temperature compensation is automatically enhanced. The system has a self-learning capability, dynamically optimizes a risk judgment threshold according to historical data, and improves the control precision.

The invention discloses an IOT (Internet of Things) equipment fault prediction method based on GraphRAG. The IOT equipment fault prediction method comprises the following steps: step 1, collecting and preprocessing multi-source heterogeneous data of various IoT equipment terminals; 2, constructing a graph structure based on the preprocessed data, a dynamic edge weight mechanism and a knowledge sub-graph; according to the graph structure, physical connection, functional dependence and communication topology between IOT devices are naturally expressed, so that the relevance between the operation states of the devices is fully captured. Sensor data, historical maintenance records and equipment configuration information are coded into node attributes and edge weights, so that the system can realize unified representation of multi-source heterogeneous data. The GraphRAG framework combines the structure learning ability of a graph neural network and the knowledge fusion characteristic of a retrieval enhancement generation mechanism, and shows stronger generalization ability in the recognition of a non-fault mode. When an abnormal signal occurs in a certain device, the model can trace the potential influence range through the state propagation path of the adjacent node, and the accuracy of early warning is improved.

The invention relates to a lightweight asynchronous message processing method and system for a high-concurrency scene, and the method comprises the steps: receiving a message request of a client, carrying out the preprocessing of the message request, and generating a to-be-processed message; distributing the to-be-processed message to a multi-level priority queue based on a priority scheduling mechanism; dynamically adjusting the number of core threads and the maximum number of threads of the thread pool according to the resource state data, and forming a thread pool resource configuration scheme of the current scheduling period; dynamically calculating the message processing quantity of the current scheduling batch based on the message accumulation quantity of each priority queue, the thread pool resource configuration scheme, the CPU utilization rate in the resource state data and the JVM heap memory utilization rate; and based on the out-of-heap memory buffer area reference, assigning the to-be-processed message object to a corresponding processing thread to execute a service processing logic until the processing operation of the messages in all the scheduling units is completed. The method has the effect of improving the resource adaptability of the asynchronous message processing architecture.

The embodiment of the invention provides a multi-modal low-altitude grid data dynamic indexing method and device for improving a cache mechanism. The method comprises the following steps: mapping multi-modal low-altitude grid data to a unified vector space, and constructing a cross-modal semantic association hybrid index for the mapped low-altitude grid data; dividing the mapped low-altitude grid data into a plurality of dynamic sub-regions according to data distribution density and data access popularity, and managing a mixed index corresponding to each dynamic sub-region by combining a Loki log index fragmentation mechanism and tagged metadata; the mapped low-altitude grid data is cached by adopting an EHCD hierarchical multi-level cache architecture; and carrying out cross-modal low-altitude grid data retrieval by combining SIMD instruction set parallelization vector similarity calculation and GPU acceleration complex space relation operation. In this way, the real-time retrieval efficiency, the system expansibility and the energy efficiency ratio of the low-altitude grid data can be remarkably improved.

The invention discloses a self-adaptive evaluation method and system for the running state of a contact network. The method comprises the following steps: constructing a contact network operation state evaluation index system, and obtaining time sequence detection data of each evaluation index; dynamically calculating the length of an adaptive window at the current evaluation moment, and extracting a nearest data subset matched with the length of the adaptive window from the time sequence detection data; using an improved CRITIC method to calculate and obtain a first objective weight vector; updating the gain coefficient vector, and calculating a second objective weight vector; combining the second objective weight vector with a subjective weight vector determined by an analytic hierarchy process by adopting a combined weight method to generate a final combined weight vector, and evaluating the running state of the overhead line system at the current evaluation moment until the evaluation task is finished; according to the method and the system, the accuracy and the timeliness of state evaluation of the overhead line system are effectively improved.

The invention provides an acute attack early warning system and method for mild and moderate asthma based on multi-source dynamic data fusion, and belongs to the technical field of intelligent medical treatment and artificial intelligence, and the method comprises the steps: obtaining static basic data, dynamic diagnosis and treatment data and environment spatio-temporal data of a patient through a multi-source data access module; seasonal interference is eliminated through a data preprocessing and hour-hour alignment module, and heterogeneous data alignment is achieved; data weights are distributed in a self-adaptive mode through a multi-modal feature fusion module of gating cross attention; a bidirectional LSTM and Transform mixed time sequence modeling module is adopted to capture short-term fluctuation and long-term trend at the same time; a risk prediction and interpretability module is combined to output an attack probability and quantify a feature contribution degree; and three-level risk layering and corresponding clinical intervention are realized through the risk grading and intervention module. The defects of one-sided single-source data, incomplete time sequence modeling and lack of clinical suitability in the prior art are overcome, the early warning accuracy and practicability are improved, and an individualized early warning and intervention scheme is provided for patients with mild and moderate asthma.

The invention discloses an intelligent scheduling method for unmanned workshop production, and the method comprises the steps: collecting the feature data of task orders, equipment loads and the like in real time, carrying out the normalization and feature dimension reduction processing, and constructing a standardized task state vector; constructing a task-resource-state causal graph based on a Bayesian network, and identifying and quantifying key factors influencing the scheduling performance; in combination with causal reasoning and anti-factual reasoning, multi-strategy generation and screening under new tasks and abnormal working conditions are realized, and online fine adjustment is performed on selected strategies through a reinforcement learning model; feedback data are collected after scheduling is executed, the causal model is dynamically corrected, continuous self-evolution and generalization ability improvement of the strategy are achieved, and the production flexibility, the resource utilization rate and the scheduling robustness of an automatic workshop can be effectively improved.

The invention discloses a data dynamic partition storage method and system based on adaptive clustering, and relates to the field of data processing. The method comprises the following steps: S1, extracting multi-scale geometric features of a high-dimensional data set, calculating a local curvature and generating a curvature feature matrix; s2, constructing a feature distance matrix and a similar matrix based on the curvature feature matrix, and generating a low-dimensional embedding matrix; s3, executing self-optimization clustering according to the low-dimensional embedded matrix, determining a cluster number through singular value distribution, and generating an initial cluster; s4, calculating a stability factor of each cluster, and triggering cluster splitting or merging operation according to a threshold value to form updated cluster division; and S5, performing incremental processing on newly added data points, obtaining a new point local curvature through local curvature gradient correction, mapping the new point local curvature to a low-dimensional space, dynamically deciding affiliation based on a cluster radius, and updating partitions. Through multi-scale feature extraction, self-optimization clustering and incremental updating mechanisms, high-dimensional data clustering precision, robustness and calculation efficiency are improved.

The invention discloses a multi-source data-based extreme weather economic influence spatio-temporal dynamic evaluation method, which comprises the following steps: collecting a multi-source heterogeneous data set, preprocessing the multi-source heterogeneous data set, and constructing a standardized data set, the multi-source heterogeneous data set comprising meteorological data, economic data, geographic information data and social data; performing dynamic weight fusion on the multi-source heterogeneous data set based on a dynamic weight fusion algorithm to generate a multi-source feature matrix; constructing a space-time coupling model through the graph neural network and the long-short-term memory network, and generating a space-time dynamic evaluation result based on the space-time coupling model in combination with the multi-source feature matrix; and visualizing a spatio-temporal dynamic evaluation result and determining a risk early warning level, generating an optimal adaptation strategy through a multi-objective decision optimization method, executing the optimal adaptation strategy, and dynamically optimizing dynamic weight distribution and model parameters through new data. According to the invention, the accuracy of time-space dynamic evaluation of the extreme weather economic influence and the early warning timeliness can be remarkably improved.

The invention discloses a dynamic intelligent test paper composition method, system and device based on multiple question types and multiple difficulty gradients, and relates to the technical field of online education, and the method comprises the steps: cleaning and removing duplications of questions in a question bank, constructing question portraits containing multi-dimensional information, and calibrating difficulty; receiving test paper configuration to generate hard constraints, dividing a multi-difficulty gradient layer, and setting target difficulty and continuity constraints; the candidate set is screened according to question types and gradient layers, and initial test papers are generated in a layered and segmented mode; the constraint conflicts are repaired through iteration replacement exchange, and finalization is optimized under the multi-objective criterion; and finally, dynamically updating capability parameters based on answer data, adjusting subsequent target difficulty, and outputting test paper and verification indexes. The follow-up test paper composition strategy can be adjusted in real time according to the answer condition of the examinee, the quality of the question bank is guaranteed, it is ensured that the test paper meets hard constraints, the difficulty is smoothly increased, the test paper quality and the evaluation effect are improved, the adaptability and accuracy of personalized testing are enhanced, and the method is suitable for various personalized testing scenes.

The invention discloses an electric power real-time data dynamic encryption transmission method and system based on a national secret algorithm. According to the method, the real-time updating of the session key is realized by fusing the SM3 Hash algorithm and the multi-dimensional dynamic factor, and the anti-attack capability of data transmission of the power system is improved. A timestamp and data counter double-trigger mechanism is introduced in the key generation process, so that the key is automatically alternated under a fixed time interval or a data volume threshold value, a key exposure window is shortened, and the security risk caused by using the same key for a long time is reduced. Key synchronization is carried out in combination with an SM2 bidirectional authentication channel, the confidentiality of key distribution is ensured, access of unauthorized equipment is avoided through an identity authentication mechanism, full-closed-loop security protection from key generation to transmission is formed, the high-security requirement of the power industry for key data transmission is met, and the security of key data transmission is improved. Through deep combination of a cryptographic algorithm system and a dynamic adaptation mechanism, the high efficiency and compliance of power real-time data transmission are considered while the security is ensured.

The invention discloses a multi-agent reinforcement learning method. The method comprises the following steps: step 1, system initialization and edge device modeling; 2, generating a data source and constructing a task package; step 3, edge equipment trajectory planning and movement acquisition; 4, local task execution and unloading strategy decision making; 5, communication link modeling and bandwidth resource allocation are carried out; step 6, scheduling optimization driven by an information age and depreciation mechanism; step 7, multi-agent strategy optimization based on Actor-Critic is carried out; and step 8, reward function design and reinforcement learning process. The technical problems that an existing centralized scheduling or heuristic algorithm cannot perform efficient learning and scheduling in a resource heterogeneous and data dynamic environment, and cannot still have good learning ability and generalization ability under the condition of lack of global information are solved; the method is suitable for system modeling and optimization work of efficient strategy learning and collaborative decision making of multiple agents in a complete cooperation task.

The invention provides a low-code platform system for data visual display. The low-code platform system comprises a data base layer, an application supporting layer and an application display layer. Wherein the integrated multi-source data is subjected to standardization processing and management and control through the data base layer, and a unified data source is generated; performing visual configuration on the unified data source through an application support layer based on a dynamic panel nested sub-canvas editor and a preset interaction configuration mechanism, and generating a visual editing data set; and carrying out multi-terminal presentation and version management on the visual editing data set by adopting a multi-terminal adaptation strategy through the application display layer, and constructing a visual display interface. Dynamic access and rendering of data are guaranteed through the data base layer, and the problem of data adaptation is solved; interaction configuration is simplified through the application support layer, multi-role cooperation is supported, the development efficiency is improved, and the period is shortened; through the application display layer, one-time development is adapted to multiple terminals, and the repeated cost is reduced.