319 results about "Decision networks" patented technology

Disclosed in the present invention is a four-network integration architecture for an unmanned swarm system. The four-network integration architecture has the capabilities of heterogeneous platform resource pooling, intelligent dynamic computing power allocation, and timely decision planning, so as to maximize the overall benefit. The present invention focuses on abstracting and integrating independent submodules to form a mesh topology of a swarm. The present invention designs a four-network integration architecture for an unmanned swarm system, which comprises a computing power network, a perception network, a decision network and a communication network as core modules. The structure aims to achieve efficient cooperation of all parts in the swarm, thereby improving the overall performance and adaptability of the system. The system integrates environmental perception, a swarm network modeling component, a knowledge base and a resource pool, providing an intelligent environmental perception strategy and a network modeling strategy for the interior of the swarm. Therefore, the perception of environments, tasks and networks by nodes can be facilitated, thereby completing establishment of intelligent networks, so as to ensure the characteristics of the stability and flexibility of networks.

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 decision-making method, device, equipment and medium based on multi-modal semantic alignment. Executing cross-modal alignment by taking the voice semantic map as a reference to generate associated information, fusing the voice features, the visual features, the action features and the associated information to generate a fusion feature vector, inputting a decision network to generate a decision feature vector and generate a task execution instruction, obtaining execution feedback information of the task execution instruction, and updating the decision network. According to the method, input is dominated by voice instructions, visual features, action features and semantic map depth alignment and fusion are combined, input naturalness and multi-modal data analysis and decision-making efficiency are improved, and interaction adaptability and decision-making accuracy of the model in a complex scene are improved.

The invention relates to the technical field of electric power engineering, and discloses an electric power engineering multi-modal RAG system based on a knowledge graph and multi-Agent collaboration, and the system comprises a multi-modal dynamic knowledge base construction module which is configured to carry out the structural processing, multi-dimensional knowledge organization and dynamic optimization of electric power engineering multi-modal data; the self-adaptive retrieval strategy engine module is configured to construct a weight decision network based on deep reinforcement learning and execute multi-channel parallel retrieval and result fusion; the iterative self-reflection reasoning module is configured to generate a reasoning path in combination with the retrieval result and verify evidence validity from multiple dimensions; the MCP tool intelligent calling module is configured to integrate multiple types of standardized MCP tools; and the multi-Agent collaborative framework is configured to provide multiple types of Agents which are specific in function and have a cross-module interaction capability. According to the method, the question and answer accuracy, the reasoning depth and the result interpretability in the complex multi-modal scene of the electric power engineering can be remarkably improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as agent autonomous decision making, financial science and technology and medical health, and discloses a multi-modal sequence data processing method, device and equipment and a medium. Extracting multi-scale feature hierarchies, combining the multi-scale feature hierarchies into a multi-scale feature pyramid set, performing cross-modal feature alignment to generate a multi-scale alignment feature sequence, executing local and global attention processing to generate long-distance dependency features, performing cross-layer information interaction to generate comprehensive multi-scale features, and performing multi-scale feature extraction; and dynamically fusing the multi-modal information and inputting the multi-modal information into a task decision network to obtain a target task result. According to the method, through the multi-scale feature pyramid, cross-modal alignment, attention processing and cross-layer information interaction, the problem of insufficient relevance between different modals and different scales in multi-modal long sequence data is solved, and fine modeling and dynamic fusion of multi-modal and multi-scale features are realized.

The invention provides a large model lightweight reasoning deployment method under limited hardware resources, and the method comprises the steps: quantifying the weight importance of a large model through a composite index of gradient sensitivity and activation frequency, and carrying out pruning operation in combination with an improved index weighted moving average strategy, thereby obtaining a structured sparse model; the sparse model is divided into sub-networks by adopting double rules, a routing decision network is trained, and an adaptive feature shunting architecture model is constructed; a multi-precision weight set is generated through a nested quantization technology, quantization bit width is dynamically adjusted, and edge equipment hardware parameters are adapted to complete reasoning environment initialization; after a reasoning request is received, an optimal sub-network is selected based on the trained routing decision network, corresponding weights are loaded in parallel, and a reasoning result is fused and output; and converting a reasoning result format, and dynamically optimizing a scheduling strategy based on a system real-time monitoring index. The method is compatible with a mainstream large model and a hardware platform, and an efficient and universal deployment scheme is provided for end-side AI engineering landing.

The invention relates to an unmanned aerial vehicle group collaboration and task allocation optimization method and system based on edge computing, in particular to the field of communication, efficient task allocation and threat early warning are achieved through dynamic modeling of a multi-modal sequence prediction model and a heterogeneous relation graph, firstly, real-time environment and historical task data are fused, and the real-time environment and historical task data are fused; generating space threat probability distribution and an environment dynamic coefficient; then, a dynamic adjacency matrix is used for adjusting a subgraph embedding vector, a threat-driven topological structure is reconstructed in real time, a decision-making layer outputs a task instruction and value evaluation based on a hierarchical decision-making network, task acceptance, task abandoning and path selection are intelligently optimized, and task conflicts are solved through a federal consensus mechanism; according to the method, the cooperation efficiency and the task execution accuracy of the unmanned aerial vehicle group in a complex environment are effectively improved, and task allocation and resource use are optimized.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as agent autonomous decision making, financial science and technology and medical health, and discloses a task processing method and device based on visual attention enhancement, equipment and a medium. Visual hierarchical features are extracted, a double fovea attention module processes and fuses high-level visual features, a side suppression network obtains enhanced visual features, and a cross-modal fusion module generates fusion features by taking the enhanced visual features as query vectors and taking language components and action components as key and value vectors; and fusing the feature input decision network to generate target category and position information, generating feedback information based on actual label difference, and updating module parameters to complete a target task. According to the invention, through combination of a bionic vision mechanism and multi-modal attention fusion, the visual feature extraction and background suppression capability is improved, and the target capture efficiency and recognition precision in a complex scene can be improved.

The invention discloses a multi-energy-storage thermal power generating unit load prediction and optimal scheduling method and system, and relates to the technical field of multi-energy-storage thermal power generating units, and the method comprises the steps: collecting the operation parameters and external environment parameters of a thermal power generating unit in real time, and constructing a real-time state parameter matrix; constructing a load prediction model based on the historical state parameter matrix, importing the real-time state parameter matrix into the load prediction model, outputting a load trend prediction curve, and triggering an early warning signal through a secondary discrimination mechanism; identifying a load disturbance value based on the load trend prediction curve, obtaining a load disturbance sequence, and decoupling the load disturbance sequence into a plurality of components; inputting the plurality of vectors into a preset decision network, dynamically correcting a constraint condition built in the decision network in combination with the early warning signal, introducing an improved dragonfly algorithm for optimization iteration, and generating an optimization scheduling instruction; according to the method, the adaptability of optimal scheduling and high-precision prediction of the load trend are improved.

The invention relates to the technical field of machining automation control, and discloses an intelligent tool changing decision-making method based on tool wear perception, which comprises the following steps: acquiring real-time state data of a tool through vibration, acoustic emission, force and temperature sensors, and fusing features of a multi-modal graph neural network to obtain tool wear feature data. And inputting the parameters into a Bayesian decision network, optimizing a tool changing strategy by using a dynamic probabilistic reasoning structure, and generating decision optimization parameters. And a multi-target tool changing optimization model with the highest machining efficiency and the longest service life of the tool as targets is constructed, and an optimal tool changing strategy is determined by adopting an improved particle swarm algorithm. Based on this, a hierarchical decision control model is established and comprises a global evaluation layer, a dynamic adjustment layer and an execution control layer, and intelligent control of tool changing action is realized. In addition, a self-healing control module is embedded in the system to deal with abnormal wear of the cutter. The machining efficiency is improved, the service life of the cutter is prolonged, the machining quality is guaranteed, and intelligent development of machining is promoted.

The invention discloses a knowledge graph-based old building reconstruction scheme automatic recommendation method. The method comprises the following steps of S1, obtaining and preprocessing old building house data; s2, extracting a semantic entity and attribute relationship from a transformation case library, a building specification library and a construction scheme library, and constructing a knowledge graph; s3, mapping the house data to knowledge graph entity nodes, and executing graph embedding to generate building semantic representation; s4, constructing a graph neural network, calculating node semantic relevancy, and generating a building state vector; s5, inputting the building state vector into the reinforcement learning decision network, and optimizing the strategy to obtain an optimal transformation action; s6, screening reconstruction measures from the knowledge graph according to the optimal reconstruction action, and performing scoring to form candidate schemes; and S7, sorting the candidate schemes, selecting the scheme with the highest score, and recommending and updating the knowledge graph. According to the method, intelligent generation and self-optimization recommendation of the old building reconstruction scheme are realized, and the reconstruction efficiency of the scheme is remarkably improved.

The invention discloses a rose planting medication scheme decision-making method based on multi-modal feature fusion. The method comprises the following steps: firstly, collecting multi-modal data of a rose planting area, and inputting the obtained multi-modal data into a feature fusion module for multi-modal feature fusion; a double-branch decision network is used for identifying the types of diseases and pests and judging the types and dosage of pesticides, and a pesticide proportioning rule is introduced through a pesticide liquid mixing control module to correct the types and dosage of the pesticides. And finally, through a user interaction optimization mechanism, closed-loop control is achieved, and a medication scheme decision is optimized. According to the method, dynamic closed-loop decision making is realized in combination with user feedback, so that individual requirements of different farmer households are met, and the pesticide application problem caused by data isolation, decision making staticization and the like in traditional rose planting is solved. An artificial intelligence technology and agronomic knowledge are deeply coupled, and traditional agriculture is promoted to be transformed to intelligence and automation.

The invention provides a discrete manufacturing capacity prediction method based on AI multi-agent collaboration, a medium and a system, and belongs to the technical field of AI multi-agent collaboration manufacturing. Equipment material human resources are abstracted into agents by constructing a distributed multi-agent collaboration architecture, and a time synchronization mechanism is configured; an intelligent agent state sensing layer is established, various resource states are monitored in real time by applying an artificial intelligence technology, an intelligent agent collaborative decision network based on a graph neural network is constructed, and stable convergence is realized by adopting a game theory and a consistency algorithm; a historical data preprocessing module is established, key production features are extracted through data cleaning and feature engineering, a processing strategy is selected according to a data missing rate, a productivity prediction result output and feedback optimization mechanism is established, and model parameters are adaptively adjusted according to prediction errors; the technical problems of low productivity prediction precision and incapability of real-time dynamic adjustment caused by isolated and dispersed heterogeneous resource state information in a discrete manufacturing system are solved.

The invention discloses a network security situation awareness method based on artificial intelligence, and the method comprises the following steps: collecting multi-source heterogeneous data, and generating a standardized data set; spatial-temporal feature decoupling is carried out, and spatial-temporal dimension features are separated; fusing the time-space cross attention, and outputting a fused time-space feature vector; constructing a causal inference engine, and outputting a dynamic causal graph and an anti-fact inference result set; constructing a dynamic risk propagation model, and outputting a whole asset risk value matrix and a risk propagation path diagram; generating a situation quantization matrix, constructing an adversarial training decision network, and outputting a defense strategy set verified by adversarial training; automatically generating a strategy; and a man-machine cooperative verification closed loop is realized. According to the method, dynamic reconstruction of a threat propagation path is realized through spatial-temporal feature decoupling and a causal reasoning engine, and a risk positioning error is reduced; and the adversarial training decision network is combined, so that the misjudgment rate of the defense strategy in the simulation APT attack test is reduced.

The invention discloses a communication system based on a satellite multimode edge computing gateway, and relates to the technical field of satellite communication and edge computing. A decentralized decision network is constructed through the multimode edge computing gateway deployed in a distributed mode and a built-in cooperative control unit of the multimode edge computing gateway; the defect of high delay caused by satellite-ground long-distance transmission in traditional centralized control is effectively overcome, so that a load balancing and routing strategy can quickly respond to a continuously changing network state in a constellation, and the agility of the system to deal with dynamic services is remarkably improved; secondly, the inter-satellite link load sensing module adopts a multi-dimensional index fusion and self-adaptive weighting algorithm, so that the difference between the real-time load pressure and the service level of the satellite node can be accurately described, a high-quality and explainable decision basis is provided for distributed cooperative scheduling, misjudgment possibly caused by a single index is avoided, and the reliability of the distributed cooperative scheduling is improved. And the adaptive capacity of the system in different service scenes is enhanced.

The present disclosure discloses a method and system for formation control for an USV swarm via a CEDRL. The method includes: designing a desired formation pattern based on a formation hierarchical virtual leader strategy, establishing an USV desired location library, and assigning a location index to a desired location of each USV in a formation; updating the desired location of each USV and the corresponding location index via an USV formation local consensus strategy in a case where there is a risk of collision between USVs; and acquiring an actual geolocation of each USV in real time, and adopting a surface vessel control decision-making network to direct the USV toward a latest desired location. An autonomous collaborative formation of a large-scale USV swarm may be realized by the present disclosure.

The invention discloses a two-stage multi-agent cooperation strategy based on exploration reward molding, and belongs to the field of multi-agent reinforcement learning. Trajectory data (including environment states, rewards, rewards and actions) generated by interaction of the intelligent agent and the environment are stored in an experience buffer pool and are updated and maintained through increment. Subsequently, randomly sampling an environment state and a corresponding return from the experience pool, and constructing a conditional diffusion model by using the return as a condition to generate a high-return target state; thirdly, global environment states of different time steps in different trajectories are sampled, time structure mapping is learned, and states with similar time are mapped to hidden states with similar geometric space; according to the method, a double-end Q network is adopted, an exploration strategy is decoupled into a target exploration strategy and a behavior exploration strategy, reward functions corresponding to two stages respectively act on a decision-making network of an intelligent agent, and more effective exploration and collaboration are achieved.

The invention discloses an operation optimization method and system for a new energy heavy truck battery power system, and the method comprises the steps: inputting the current vehicle operation condition data of a new energy heavy truck into a pre-constructed decision network architecture, and outputting a state evaluation result and an action evaluation result; combining the state evaluation result with the action evaluation result to generate a plurality of candidate control strategies for operation optimization of the new energy heavy truck battery power system; the method comprises the following steps: performing feature extraction on multi-modal operation data from different sensors of a new energy heavy truck battery power system by adopting a self-supervised learning method to obtain unified feature representation; and performing matching evaluation on the unified feature representation and candidate control strategies, and selecting an optimal control strategy from a plurality of candidate control strategies to perform operation optimization of the new energy heavy truck battery power system. According to the invention, operation optimization can be accurately and efficiently carried out on the new energy heavy truck battery power system.

The invention relates to the technical field of intelligent switching, in particular to a multi-mode dynamic coupling intelligent switching method and system based on road condition recognition and a vehicle. The method comprises the steps that gradient, vehicle speed, accelerator and brake signals are collected in real time, and a multi-dimensional feature vector fusing driving habits is constructed after preprocessing; a real-time road condition label is automatically generated through unsupervised clustering, and the clustering effectiveness is verified online by using a contour coefficient. Enabling the road condition label and the current power mode to form a system state, and inputting the system state into a self-learning decision network based on reinforcement learning; and the network updates the state-action value matrix through a reward function in a time sequence difference mode, and outputs a target power mode with the highest accumulated reward. And calculating the motion confidence coefficient of the target mode based on the value matrix, if the motion confidence coefficient exceeds a threshold value, generating a switching instruction, and smoothly adjusting the torque by an execution mechanism through a torque transition algorithm to realize impact-free switching. Automatic road condition recognition, self-adaptive decision making and non-inductive execution are achieved, and smoothness and adaptability are improved.

The invention discloses an unattended power distribution room intensive power distribution system and method based on machine learning, and the method comprises the steps: collecting the operation data of an unattended power distribution room, executing the preprocessing, and generating a standardized input data set; constructing a topology, working condition and equipment feature association relationship to form a three-dimensional coupling feature structure; inputting a hierarchical graph time sequence decision network, and fusing topology and time sequence characteristics to output candidate control actions; constructing a dynamic feasible region according to the joint features, and screening a safety action set meeting constraints; inputting into a topological reversible neural differential control module, predicting transient response and optimizing an execution action; and issuing a control instruction, collecting feedback, and triggering model increment update to realize closed-loop optimization. According to the invention, by introducing a hierarchical graph time sequence decision network and a topological reversible neural differential control mechanism, intelligent intensive scheduling and self-learning closed-loop optimization control of the unattended power distribution room are realized.

The invention discloses a cargo loading method, device and equipment based on deep reinforcement learning, and the method comprises the steps: scanning a loading space through a multi-mode sensor, obtaining three-dimensional point cloud data, carrying out the construction based on the three-dimensional point cloud data, and obtaining a three-dimensional container model corresponding to the loading space; carrying out identification and 6D pose estimation on the to-be-loaded goods to obtain size pose information corresponding to the to-be-loaded goods; generating a plurality of candidate placement poses according to the three-dimensional container model and the size posture information; determining an optimal placement pose in the candidate placement poses by adopting a pre-constructed deep reinforcement decision network, and executing placement operation of the to-be-loaded goods in the optimal placement pose by controlling a mechanical arm; and after the placement operation is completed, re-scanning the loading space to obtain the current three-dimensional model, calculating an execution error, and updating the three-dimensional container model and network parameters of the deep reinforcement decision network by using the execution error.

The invention provides a special equipment inspection system and method based on a multi-modal large model. The method comprises the following steps: collecting and preprocessing multi-modal data of special equipment; constructing a space-time fusion attention network for feature extraction to obtain fusion feature representation; performing inference analysis by using a pre-trained multi-modal large model, and distinguishing normal and abnormal states through comparative learning; a dynamic Bayesian decision network is constructed in combination with historical data and an expert knowledge base, and fault early warning and maintenance suggestions are generated; optimizing an inspection strategy according to the importance of the equipment and the early warning level; the inspection result is displayed through augmented reality, an abnormal area is marked by means of a digital twin technology, and maintenance guidance is provided. Through the scheme of the invention, the fault prediction accuracy can be improved, false alarm and leak detection can be reduced, the inspection path can be optimized, the maintenance cost can be obviously reduced, the service life of equipment can be prolonged, and the production safety can be improved.

The invention discloses an underwater vehicle deep reinforcement learning formation control method based on an imitation learning compensator, and an underwater vehicle reinforcement learning model comprises a first decision network and N second decision networks, input of a first decision network is a state of a first underwater vehicle, output of the state of the first underwater vehicle is an action of the first underwater vehicle, input of a second decision network is a state of a second underwater vehicle corresponding thereto, and output of the action of the second underwater vehicle is an action of the second underwater vehicle when the second underwater vehicle cannot acquire the action of the first underwater vehicle According to the underwater vehicle deep reinforcement learning formation control method, state perception is provided for the second underwater vehicle, so that the second underwater vehicle accurately prejudges the navigation intention of the first underwater vehicle, and a better formation is kept.

A method and a system of bearing fault diagnosis based on a dual attention mechanism to strengthen a hierarchical decision network are provided, where the method includes the following steps: collecting bearing vibration signals in different health states; based on the bearing vibration signals, constructing a hierarchical multi-class fault diagnosis model; and determining a fault position and a fault size of a bearing by using the hierarchical multi-class fault diagnosis model.

The invention discloses a bearing fault diagnosis method based on time-frequency double-flow complementation and adaptive gating fusion, and the method comprises the steps: collecting an original vibration signal of a bearing, segmenting an original vibration signal sequence into a plurality of sample segments with fixed lengths, and forming an original data set; dividing the original data set into a training set and a test set by adopting a physical segmentation strategy based on a time axis; constructing a time-frequency double-flow feature extraction module, and extracting a time-domain feature vector and a frequency-domain feature vector; constructing an adaptive gating fusion module to dynamically generate a gating weight, and performing weighted complementary fusion on the time domain feature vector and the frequency domain feature vector to obtain a fused fault feature vector; constructing a classification decision network to map the fusion fault feature vector to a decision space, and optimizing model parameters by using a label smooth regularization loss function and a cosine annealing strategy; and inputting the test set into the trained fault diagnosis model, calculating posterior probability distribution of a sample belonging to each fault category, and outputting a bearing fault diagnosis result.

According to the large language model reasoning optimization method and device, the storage medium and the computer equipment provided by the invention, the multi-dimensional monitoring data is acquired, and then the multi-dimensional monitoring data is input into the pre-trained environment decision network to obtain the optimal model configuration. The current resource state is evaluated according to the multi-dimensional monitoring data, and the optimal execution path is determined according to the resource state and the optimal model configuration. And finally, adjusting the target model based on the optimal model configuration, and performing model reasoning according to the optimal execution path in the reasoning process of the target model. In the process, the resource state is evaluated and the optimal model configuration is determined by collecting the multi-dimensional detection data, and then the optimal execution path is dynamically determined, so that the model structure and the calculation strategy can be dynamically adjusted according to the state during actual operation, and the response delay is effectively reduced and the resource utilization efficiency is improved while the reasoning precision is guaranteed.

A time-series production simulation method taking capacity dynamic programming into consideration, and an apparatus and a medium, relating to the technical field of power systems. The method comprises: establishing a single-year time-series production simulation model; on the basis of the single-year time-series production simulation model and a CNN-GRU network, constructing an equivalent load support rate interval decision network; on the basis of the single-year time-series production simulation model, constructing a multi-stage dynamic programming model oriented to the problem of power capacity expansion; using a NO search algorithm to compress a state space in the multi-stage dynamic programming model, so as to obtain a compressed state space for the problem of dynamic programming; and in the compressed state space, using an APO optimization algorithm and a DCS algorithm to construct a time-series production simulation method solution framework taking capacity dynamic programming into consideration, and using the time-series production simulation method solution framework to determine a fast time-series production simulation result taking capacity dynamic programming into consideration.

The invention discloses a centrifugal machine dynamic filtering efficiency improving method based on artificial intelligence optimization control, and relates to the technical field of centrifugal machine filtering efficiency optimization. Constructing an edge-cloud collaborative architecture system; the sensing layer deploys various sensors to acquire equipment operation data; the edge calculation layer processes the data and makes a decision; the network transmission layer ensures reliable transmission of data; the cloud platform is used for model training and global optimization; through preprocessing and feature extraction of collected data, the state is evaluated by using a hybrid model fusing deep learning and physical simulation, control parameters are optimized based on a multi-objective optimization function in combination with fuzzy logic and an adaptive algorithm, and dynamic feedback adjustment is realized by using electro-hydraulic servo and machine vision. According to the technology, intelligent operation of the centrifugal machine is achieved, compared with traditional control, the filtering efficiency is improved, energy consumption is reduced, the service life of filter cloth is prolonged, equipment fault diagnosis is more accurate, maintenance response is faster, production benefits are improved, and product market competitiveness is enhanced.

The invention relates to a dynamic routing optimization method and device, computer equipment, a readable storage medium and a program product. The method comprises the following steps: acquiring a topological space feature vector and a predicted time sequence feature according to a node load and a link time delay of a network topology, fusing the topological space feature vector and the predicted time sequence feature to obtain a target fusion feature, and according to the target fusion feature, obtaining a target fusion feature; determining a target routing action from all routing actions in the routing action space, executing a traffic forwarding operation corresponding to the target routing action, obtaining a network performance parameter after the traffic forwarding operation is executed, obtaining a target fusion reward value based on the network performance parameter and the state prediction parameter, and sending the target fusion reward value to the routing action space; and updating model parameters of the decision network model according to the target fusion reward value, and performing dynamic routing optimization based on the updated decision network model. By adopting the method, the communication reliability can be improved.

The invention discloses a grid-connected inverter fusion control method based on voltage and current source time sharing, and relates to the technical field of electric power automation, and the method comprises the steps: carrying out the two-phase rotating coordinate system conversion of power grid coupling data, and generating a voltage-current joint component; performing power error analysis on the voltage-current joint component, outputting a power grid state vector, performing modal decomposition on the power grid state vector, obtaining a dominant oscillation mode matrix, performing Lyapunov exponent calculation on the dominant oscillation mode matrix, forming a stability pre-judgment mark, and inputting the stability pre-judgment mark into a time-sharing fusion decision network model to obtain a time-sharing fusion decision network model. The characteristic coupling layer carries out cross physical quantity correlation, and the stability evaluation layer carries out stability boundary calculation and outputs a voltage-current source control signal. Through the intrinsic orthogonal decomposition algorithm and the time-sharing fusion decision network model, the response speed and the fusion control capability of the grid-connected inverter are improved.

The invention discloses a decision recommendation method and device, computer equipment and a storage medium, belongs to the technical field of artificial intelligence, and is applied to a car insurance risk control decision scene. According to the method, real-time fusion and dynamic feature alignment of the multi-source heterogeneous data are realized by constructing the federal learning data center, and the bottleneck of traditional data islands and scattered storage is broken. A pre-trained dynamic deep learning model is utilized, a space-time attention mechanism and an incremental updating engine are introduced into the system, space-time association of sensor data and business events is effectively captured, and cross-scene generalization ability improvement and quick response to market fluctuation are achieved. The explainable decision network splits complex model output into clear feature contribution degrees, and by matching with key influence factor extraction and natural language decision correction, the transparency of decisions and the understanding efficiency of business personnel are greatly improved. The real-time recommendation system based on reinforcement learning guarantees the timeliness of abnormal early warning and the accuracy of decision execution.