4111 results about "Edge node" patented technology

The invention discloses a data center intelligent operation and maintenance method and system based on an industrial internet of things, belongs to the field of image processing calculation, and aims to solve the problems of privacy protection deficiency, serious manual dependence and weak energy efficiency and safety. Full-link dynamic optimization is realized through an edge calculation layer and an adaptive clustering network, edge nodes are integrated with a wavelet transform noise filtering technology, dynamic denoising is performed on multi-band sensor signals of temperature, vibration, current and the like, heterogeneous device time sequence data are aligned in combination with a dynamic time warping algorithm, sampling frequency differences of multiple devices are eliminated, and the multi-band sensor signals are obtained. A weight formula is put forward, a cluster head is dynamically elected based on node residual energy, data similarity and a load coefficient, a transmission path of a high-delay area is optimized in combination with a multi-hop routing protocol, the packet loss rate and the signal strength are evaluated in real time, redundant coding is started for a high-packet-loss link or the high-packet-loss link is switched to a low-power-consumption mode. And synchronizing the dynamic topology state with the digital twin platform through an open platform communication unified architecture protocol.

The invention discloses an edge computing resource allocation optimization method and system based on reinforcement learning, and relates to the technical field of edge computing resource management.The method comprises the steps that computing load data and network state data are collected, a historical load sequence is formed, a state space matrix is constructed, the historical load sequence is mapped into a discrete state value, and the discrete state value is obtained; establishing a deep neural network model; inputting the state space matrix into a deep neural network model, and generating a resource allocation strategy set according to the priority and time delay requirements of the calculation tasks; iteratively training the resource allocation strategy set by adopting a double Q learning algorithm, setting a reward function based on task completion time and a resource utilization rate, and selecting an optimal resource allocation strategy; and adjusting computing resource allocation of the edge nodes according to the optimal resource allocation strategy, and meanwhile, feeding back an execution result to the deep neural network model for online learning updating. According to the method, intelligent optimization of edge computing resource allocation is realized by introducing a deep reinforcement learning method.

The invention provides an intelligent Internet of Things public security management and control system and method based on multi-source data fusion, and belongs to the technical field of Internet of Things security. According to the system, unified collection and standardized processing of multi-source data are achieved by recognizing a system operation scene and loading corresponding model parameters and response strategies, and a unified semantic representation structure is constructed. And the system executes anomaly detection at an edge node, completes risk scoring and grading alarm judgment in combination with a semantic structure and anomaly feature information, and generates a control response instruction based on strategy matching. Meanwhile, dynamic optimization of the scoring model and identity authentication, behavior auditing and data compliance export in the operation process are supported. The system has intelligent identification, adaptive analysis and response closed loop capabilities, and improves the precision and credibility of public security management in the Internet of Things environment.

The invention relates to the technical field of micro-grid control, in particular to a multi-layer distributed micro-grid control system and method based on edge cloud collaborative lightweight reinforcement learning. The method comprises the following steps: a cloud management unit trains a DQN model by using historical environment data, generates a lightweight DQN model through pruning and quantification, issues the lightweight DQN model to an edge node unit, and formulates day-level and weekly-level optimization scheduling strategies based on a function layering + layering cooperation strategy; the edge node unit monitors electrical quantity and environmental parameters in real time through a sensor, deploys a lightweight DQN model to monitor abnormal conditions and trigger local early warning and emergency strategies, and makes a fast decision by using the lightweight DQN model; the micro-grid subgroup units collect data of the edge node units. According to the invention, through close cooperation of the edge node layer, the micro-grid subgroup layer and the cloud management layer, a distributed cooperative control mode of edge node real-time decision, subgroup cooperative management and cloud global optimization is realized.

The invention relates to the technical field of energy scheduling, in particular to a multi-modal data fusion method and system based on energy scheduling and a storage medium. The method comprises the following steps: obtaining multi-modal data, and carrying out abnormal fluctuation feature extraction to obtain a space-time fusion abnormal feature labeling set; deducing a multi-objective optimization path according to the space-time fusion abnormal feature labeling set to obtain a dynamic scheduling decision map; performing edge node game equilibrium calculation according to the dynamic scheduling decision map to obtain a trusted scheduling verification chain; performing digital twinborn constraint optimization on the trusted scheduling verification chain to obtain a closed-loop scheduling digital twinborn body; compiling a dynamic scheduling instruction set based on the closed-loop scheduling digital twin to obtain an anti-disturbance energy scheduling strategy library; and obtaining real-time energy supply and demand data, and performing scheduling deviation tracing on the real-time energy supply and demand data according to the anti-disturbance energy scheduling strategy library to obtain an energy distribution decision. According to the invention, the efficiency and reliability of energy scheduling can be improved.

A block chain double-chain cold chain traceability management system for meat quality fluctuation belongs to the technical field of traceability management systems and comprises a data acquisition and preprocessing module, a composite index module, a security verification module, a federated learning and AI resource management module, a fragmentation and verification architecture module and an automatic error correction module. According to the method, data chain storage environment data hash and transaction chain record operation logs are innovatively separated through a block chain double-chain architecture, hash fingerprints are synchronized in combination with an anchoring technology, cross-chain consistency is ensured, long-term non-tampering of data is realized through an anti-quantum hash algorithm, resource allocation is optimized through hierarchical storage design, and the method is suitable for large-scale popularization and application. The composite index technology is fused with hypergraph dynamic index to trace production batches, vehicles, warehouses and space-time quadtree dynamic subdivision areas, complex query response time is remarkably compressed, path deviation real-time alarm is achieved, the edge node cache hit rate is increased, and full-link data safety and query efficiency are remarkably improved.

The invention belongs to the technical field of data transmission, and particularly relates to an SD-WAN low-delay data transmission method and system based on edge computing, a plurality of edge computing nodes are deployed in an SD-WAN architecture, and the edge computing nodes are distributed on a network edge side, close to terminal equipment or a branch mechanism and have data preprocessing and local computing capabilities; monitoring network state parameters of each link in the SD-WAN in real time, wherein the network state parameters comprise delay, bandwidth, packet loss rate and computing resource utilization rate of edge nodes; routing to-be-transmitted data to the target edge node, and if the node has local processing capability, executing data preprocessing or caching operation; otherwise, forwarding the data to an adjacent edge node or a cloud data center; a distributed cache strategy is adopted, when network congestion is detected according to data access frequency and timeliness requirements, non-real-time data is temporarily stored in a local cache, and asynchronous transmission is executed after a link is recovered, so that the method has the effect of providing higher-quality and more reliable network service for a user.

The invention discloses an electric power capital construction process early warning method and system based on a high-precision three-dimensional model, and relates to the technical field of electric power capital construction, and the method comprises the steps: collecting multi-source data of an electric power capital construction site, and constructing a high-precision three-dimensional model comprising equipment space coordinates, construction time sequence labels and environmental parameters; inputting the space-time diagram into a space-time diagram convolutional network, outputting a risk propagation probability matrix of a three-dimensional space and a construction time sequence dimension, and identifying high-risk nodes and associated construction stages; and dynamically marking root cause coordinates and a risk diffusion range in the high-precision three-dimensional model, generating an early warning instruction containing a visual path, a root cause report and a disposal priority, and pushing the early warning instruction to a terminal in real time through an edge node. Based on the hierarchical space-time attention-physical constraint graph convolutional network, the inter-node cross-stage association weight is dynamically allocated, the convolution kernel parameters are optimized in combination with the physical connection strength, and the space-time association of high-risk node identification is significantly improved.

The invention relates to the technical field of machine tool intelligent monitoring, and discloses a numerical control machine tool intelligent monitoring method and system based on the Internet of Things and a storage medium. The method comprises the steps of collecting machine tool multi-source data and transmitting the data to an edge node; original data is processed to obtain a working condition feature set, the working condition feature set is input into a recursive convolution hybrid memory network to construct a common dependency relation graph, and state parameter prediction is realized through time-frequency domain joint analysis; converting the two-dimensional feature map into a two-dimensional feature map, and extracting interaction features through SE-ResNet; fusing the two types of features to execute state classification; and predicting the life and generating a diagnosis report based on the health state judgment result in combination with the process parameters and historical data. According to the method, effective fusion analysis of multi-source heterogeneous data of the numerical control machine tool is realized, and a self-adaptive state recognition and fault early warning mechanism is constructed, so that accurate monitoring of the operation state of the machine tool, intelligent diagnosis of early faults and accurate prediction of residual life are realized.

The invention discloses an operation and maintenance method and system of a multi-cloud management platform based on cloud edge collaboration, and the method comprises the following steps: S1, deploying cloud service and edge nodes, and initializing a cloud edge collaboration environment; s2, constructing an edge autonomous optimization network, adjusting and optimizing a task strategy, and predicting a task load of an edge node; s3, adjusting task priorities, and classifying task strategies; s4, constructing a task feature matrix, a cloud edge resource matrix and a task and resource matching model; s5, allocating resources to each task to generate a task resource allocation table; and S6, identifying operation abnormity through the time sequence abnormity detection model, and triggering task migration, resource switching or task redistribution when detecting that the edge node is abnormal. According to the method, the edge autonomous optimization network and the intelligent scheduling algorithm are utilized, dynamic task allocation and exception recovery are achieved, and the method has the advantages of being high in resource utilization rate, high in scheduling real-time performance and good in system stability.

An Al -based energy edge platform is provided herein with a wide range of features, components and capabilities for management and improvement of legacy infrastructure, coordination, and orchestration with distributed systems to support important use cases for a range of enterprises. An Al -based energy edge platform may include a graph neural network including a set of nodes respectively representing at least one distributed energy resource (DER) and a set of edges respectively interconnecting the set of nodes, wherein each edge represents at least one energy - related feature among at least two nodes of the set of nodes. The platform may incorporate emerging technologies to enable ecosystem and individual energy edge node efficiencies, agility, engagement, and profitability. Embodiments may forecast, plan for, and manage the demand and utilization of energy in greater distributed environments. Embodiments may employ intelligent provisioning, data aggregation, and analytics to leverage energy market connection, communication, and transaction enablement platforms.

The invention provides a computing resource optimization method and system for analysis tasks, and belongs to the technical field of computers.The computing resource optimization method comprises the steps that the priority of each analysis task is determined according to feature information of each analysis task and current available computing resource state information of the system, and a priority queue is generated; obtaining a task load prediction value according to the historical task load data and the real-time system state data; according to the type of the analysis task, the data scale and the data source position, the analysis task with the real-time requirement higher than a preset standard is allocated to an edge node to be executed, and resource allocation of the edge node is dynamically adjusted according to a task load predicted value; and dynamically allocating available resources from the computing resource pool according to the priority queue and the task load prediction value so as to execute the plurality of parallel analysis tasks. According to the resource management method based on task priority dynamic adjustment, task load prediction and edge computing optimization scheduling, real-time prediction and dynamic self-adaptive scheduling of computing resources are achieved.

The invention discloses a big data auxiliary key generation method and system in communication data encryption transmission, and relates to the technical field of big data analysis and processing. The dynamic entropy source processing module is used for generating a high-randomness entropy pool by combining an information entropy quantification model and adopting a Shannon entropy and minimum entropy fusion algorithm; the anti-quantum key generation module is used for generating a dynamic variable-length key seed based on an entropy pool driven post-quantum cryptographic algorithm; the hierarchical key negotiation module adopts a clustering Diffie-Hellman protocol, dynamically divides negotiation according to network topology, and precomputes and reduces the load of a core network through edge nodes; and a lightweight verification and update module. According to the method, high-entropy sources such as environmental noise, user behaviors and equipment hardware fingerprints are fused with low-entropy sources such as network messages and sensor data, and an intelligent acquisition strategy and a nonlinear decorrelation technology are combined, so that the anti-quantum dynamic entropy pool is generated, and the randomness of a secret key and the reliability of the entropy sources are improved.

The invention relates to the technical field of tunnel power supply and distribution, in particular to a tunnel modular prefabricated cabin power supply and distribution intelligent substation self-adaptive regulation and control system based on edge calculation. Comprising an edge calculation and AI decision-making unit which is used for realizing rapid acquisition, processing and instant decision-making of tunnel power supply and distribution multi-dimensional data, generating a power supply and distribution adaptive regulation and control strategy by deploying calculation resources and a machine learning algorithm at edge nodes close to a data source, and converting the strategy into an executable regulation and control instruction; a cloud platform collaborative management unit; and an intelligent sensing and internet-of-things unit. According to the invention, hierarchical decision control of the tunnel power supply and distribution system is realized by constructing a hybrid architecture of edge computing and cloud platform collaboration and a priority judgment mechanism; according to the invention, multi-modal data are integrated through the multi-protocol communication link module and the full-scene data fusion analysis module, and data association analysis is realized through Kalman filtering, D-S evidence theory and other algorithms.

The invention discloses a resource scheduling method, system and device in an edge cloud computing platform and a medium, and relates to the field of resource scheduling. In the method, a corresponding digital twinborn model is constructed for each edge node according to a resource state and an environmental parameter, a resource change condition in a future preset time is predicted according to the digital twinborn model, and a global resource state map is generated according to the resource change condition; obtaining a task demand of each task, and determining an execution position and a priority of each task according to the task demand and the global resource state map; generating a global allocation scheme through a federated learning algorithm according to the global resource state map, the execution position and the priority; and acquiring a resource allocation state in real time, determining the difference between the resource allocation state and the global allocation scheme, and correcting the global allocation scheme based on the difference through a hierarchical collaboration mechanism. By implementing the technical scheme provided by the invention, the effects of optimizing resource allocation efficiency, reducing delay and energy consumption and improving task processing performance are achieved.

The invention discloses an Internet of Things data integration method and system based on dynamic semantic atlas and edge collaboration, and the method comprises the steps: carrying out the equipment access and protocol conversion, capturing the original data of equipment through an edge node, analyzing the average communication interval of 100ms + / -20ms through an LSTM model, generating an equipment fingerprint hash value, analyzing a message structure through a TinyBERT model, and extracting a key field, a temperature value and a timestamp. The method comprises the following steps: establishing a dynamic semantic graph, converting the dynamic semantic graph into a standard JSON format, uploading the standard JSON format to a cloud end through a service grid, constructing a dynamic semantic graph, collecting multi-source metadata by the cloud end, generating an entity relationship by utilizing a Transform model, dynamically optimizing a mapping rule based on reinforcement learning, automatically associating a Pressures field to a database column PSI, and carrying out collaborative optimization by an edge cloud; according to the method, the knowledge graph and the Transform model are combined, the data entity relation is updated in real time, the semantic drift problem of a traditional static graph is solved, the cross-domain data association accuracy is improved by 35%, the unique device fingerprint is generated through the message frequency and load distribution, and dynamic tracking of the mobile device is supported.

The invention relates to the technical field of data processing, and discloses a real-time data processing analysis method and system of an industrial PLC. The method comprises the following steps: transmitting temperature, pressure, current, vibration and acoustic parameters acquired by multiple sensors to an industrial PLC (Programmable Logic Controller) in real time to obtain multi-source heterogeneous original data; preprocessing the multi-source heterogeneous original data to obtain standardized fusion data; correlation calculation and anomaly recognition are carried out through the multivariate analysis model, and an abnormal state classification result is obtained; dynamically adjusting data interaction frequency and sampling rate between the edge nodes and the central PLC, and generating a real-time control decision instruction; and matching the real-time control decision instruction with the current motor load fluctuation state, and outputting the optimal frequency conversion control parameter. According to the invention, the response delay of the system is reduced, the control precision and reliability are improved, the dynamic balance between the safety and the energy efficiency is realized, and the system can intelligently adjust the control strategy according to the real-time safety situation.

The invention provides a cloud collaborative three-dimensional building modeling rendering method and system based on WebGL (Web Graphics Library). The method comprises the following steps: acquiring design parameters of a target building, analyzing the design parameters into geometric topological data of a plurality of independent components on the basis of a functional attribute division rule, and generating a multi-stage detail geometric model; adding material physical attributes to the surface vertex data, calculating a diffuse reflection coefficient and specular reflection intensity through a ray tracing algorithm, and generating lightweight component data carrying physical rendering parameters; in response to a view angle switching instruction of a terminal user, according to a space block corresponding to a current view angle, a main GPU node is distributed from a distributed GPU cluster, the main GPU node is divided according to the space block, adjacent blocks of the space block are distributed to edge GPU nodes, parallel ray tracing calculation is executed based on a WebGL protocol, and the parallel ray tracing calculation is executed based on the WebGL protocol. And returning to the main GPU node synthesis terminal display image and displaying. According to the technical scheme provided by the invention, the response speed is remarkably improved while the physical-level rendering effect is kept.

A federated mining method for multimodal data based on multiple security policies includes: a federated learning framework is used as a data mining model for distributed data mining; a multiple authentication mechanism is designed; a local edge node is selected to participate in a federated computation to obtain authenticated local edge node and aggregate a dataset; a multimodal data fusion and a multimodal data classification are performed on the dataset by a generalized multimodal data feature fusion model based on a multi-headed attention mechanism; and an adaptive perturbation mechanism based on cyclic correlation analysis and differential privacy is designed to add noise round by round and dynamically. A federated mining method for multimodal data is further provided.

The invention provides a secure channel networking method based on edge computing power, and the method comprises the steps: obtaining the security parameters of a link between nodes in a network topology structure based on a selected networking strategy, including link encryption strength, an authentication mechanism and a key updating period, and combining the bandwidth and time delay of the link, obtaining an optimal secure communication route between the nodes through an edge calculation method; issuing the optimized node communication route to each edge computing node, and dynamically adjusting an authentication mechanism and a key management strategy of a secure channel between the nodes according to link security parameters including link encryption strength, a delay jitter threshold and a packet loss rate threshold; and according to the updated networking strategy, dynamically optimizing node roles and link redundancy, updating a network topology structure, and dividing edge nodes into three node function roles of computing nodes, storage nodes and forwarding nodes in combination with node computing capability, storage capacity and energy consumption level.

The invention provides an intelligent pre-examination triage method based on multi-modal data and edge calculation, and relates to the technical field of multi-modal data, and the method comprises the steps: collecting the facial expression, wound part, sound and physiological signals of a patient through medical equipment; multi-modal features are extracted by using a deep learning network and dynamic weighted fusion is carried out to obtain a patient state evaluation result; performing data time sequence alignment and feature extraction based on edge nodes, dynamically adjusting calculation precision and realizing task migration; multi-source data are collected for time sequence alignment calibration and feature fusion, the emergency degree is calculated, and group damage distribution is predicted. According to the method, real-time processing and dynamic optimization of multi-modal data are realized, and the accuracy and efficiency of pre-examination triage are improved.

The invention relates to a power edge cloud cooperative management method based on a swan OS, and the method comprises the following steps: S1, deploying intelligent edge nodes, setting an embedded AI acceleration unit, operating the swan OS on all the intelligent edge nodes, and achieving the seamless cooperation of equipment through the distributed characteristics of the swan OS; and S2, realizing self-organizing connection of edge devices by adopting a Mesh network technology, designing a direct communication path between the edge devices, and realizing rapid processing of local problems. S3, aiming at the real-time requirement of a power system, optimizing an MQTT / CoAP protocol stack, improving the efficiency and reliability of message transmission, and allocating special network resources for different types of power applications by utilizing the 5G network slice characteristics, S4, constructing a security architecture based on a zero-trust principle, enhancing the equipment access control and data transmission security, and S5, establishing a security architecture based on a zero-trust principle. And identity authentication and data integrity protection are performed by using a block chain technology, so that data tampering and counterfeiting are prevented. According to the method, the local distributed decision-making capability and the global cooperation capability are effectively improved.

The invention discloses a multi-modal data real-time identification and cooperative processing system based on edge computing and federated learning. The multi-modal data real-time identification and cooperative processing system comprises a cloud center coordination node, a plurality of edge computing nodes, a cross-modal encryption engine, a federated learning controller and a model updating verification module. The cloud center coordination node executes federated learning model aggregation and dynamic task allocation, and generates a cross-modal encryption strategy; and the edge computing node is configured with a multi-modal data acquisition module, a local model training unit and a co-processing gateway to realize multi-modal data acquisition and local processing. The system encrypts vision, acoustics and text data by using differentiated algorithms such as spatial confusion, frequency domain permutation and homomorphic encryption; the federated learning controller carries out multi-modal feature fusion, hierarchical encryption and dynamic networking at the edge node; and the model updating verification module performs aggregation updating after ensuring parameter consistency by using secure multi-party calculation. According to the method, real-time processing and privacy protection of multi-modal data are realized, and the data co-processing efficiency is improved.

The invention discloses an electric power data anomaly detection method and system combined with edge computing, and particularly relates to the field of electric power data anomaly detection.The electric power data anomaly detection method comprises the steps that a periodic abnormal behavior chain structure is established by extracting a continuous judgment result, actual physical feedback and a historical standard response track of edge nodes to electric power data; and a weighted residual trend path is generated, so that identification of a node judgment offset state and quantification of an abnormal trend are realized. By constructing a power data abnormal behavior chain and a multi-cycle residual error trend graph, an edge node misjudgment offset state is identified, and path suppression and channel structure correction are executed, so that the node safety judgment accuracy and the abnormal response stability in an edge computing environment are improved.

The invention discloses a cloud-edge collaborative intelligent storage node dynamic deployment method and system. The method comprises the following steps: monitoring performance indexes such as edge node data traffic and storage resource state in real time; a deep learning algorithm combining time sequence analysis and an LSTM neural network is adopted to analyze traffic information, and a data access demand is predicted; edge nodes and cloud storage resource configuration are dynamically adjusted based on a prediction result, and an intelligent scheduling algorithm, a data cold and hot separation strategy and a self-adaptive fragmentation technology are introduced to allocate resources; the storage node layout is optimized in real time, and an optimal data distribution path is selected through a reinforcement learning strategy; data security and access control are realized by adopting end-to-end encryption, multi-level access control and block chain technologies. The system comprises a monitoring acquisition module, a prediction analysis module, a resource scheduling module, a path optimization module and a security control module. According to the method, the utilization efficiency of storage resources is improved, data delay is reduced, system stability and data security are enhanced, and the method is suitable for a cloud edge collaborative storage scene.

The invention discloses a network attack AI detection analysis method and system based on the smart Internet, and belongs to the technical field of network security protection, and the method comprises the steps: building an attack feature library through distributed edge nodes in a cooperative manner, generating feature parameters of each node based on a local attack event, and transmitting the feature parameters to a central server for dynamic fusion through encryption; constructing a multi-modal interaction graph, identifying a potential attack link based on association strength among graph nodes, and deducing an attack intention to generate a defense strategy; deploying a virtualized network environment, dynamically injecting induction characteristics, and adjusting an induction strategy in real time according to the interaction behavior of an attacker; and monitoring an abnormal mode of the user behavior sequence, triggering an AI interaction verification process and storing a defense strategy. According to the method, rapid collection and fusion of network attack features are realized, the detection delay of network attacks is reduced, the accuracy of attack prediction is improved, the flexibility and effectiveness of network attack confrontation are enhanced, and the defense intelligence and adaptive ability of the whole network are improved.

The invention relates to the technical field of Internet of Things communication, and discloses an Internet of Things data information transmission method, a switch and a transmission system. The method comprises the following steps: acquiring multi-source data streams of Internet of Things terminal equipment, dynamically fragmenting according to types to generate fragmented data packets, matching a target transmission protocol for the fragmented data packets, generating a dynamic routing path in combination with a priority mark and the like, and transmitting the dynamic routing path. The system also has the functions of link quality evaluation and route adjustment, redundant coding packet loss recovery, transmission delay and integrity monitoring processing, encryption transmission based on security level, resource optimization, transmission strategy adjustment and the like. The switch is integrated with a data fragmentation module, a protocol matching module and the like. The transmission system comprises a terminal device cluster, an edge node network, a cloud server and a protocol conversion gateway. The data transmission efficiency, reliability and safety are improved, resource utilization is optimized, and the data transmission problem of the Internet of Things is effectively solved.

The invention discloses a power distribution network collaborative management method and system based on artificial intelligence, and relates to the technical field of electrochemical detection, and the method comprises the steps: constructing a distributed edge computing node network, deploying nodes at key positions of a power distribution network, achieving the collection and preprocessing of local power data, and reducing the cross-regional data transmission pressure; an AI real-time communication scheduling model is established based on the preprocessed data, communication resources are dynamically allocated according to the operation state of the power distribution network, and fault data transmission is guaranteed preferentially; seamless interaction of multi-protocol equipment is realized through a self-adaptive protocol conversion mechanism containing protocol identification, format conversion and data verification; training a fault diagnosis model by using a federated learning framework, and enabling edge nodes to only upload parameters to a coordination center for aggregation and updating, so as to balance model precision and data privacy; when a fault is detected, a millisecond response mechanism is started, and a processing strategy is generated and executed in combination with edge local decision and central global optimization.

The invention provides an intelligent data production method and system based on a graph neural network and adaptive learning, and the method comprises the steps: collecting a text, a time sequence, an image and sensor data in real time, and converting unstructured data into a structured JSON format through a format analysis tool; extracting text entity features based on a large language model, modeling a cross-modal entity association relationship in combination with a graph neural network, and fusing multi-source heterogeneous data through a dynamic knowledge graph; the detection threshold is dynamically optimized by adopting a reinforcement learning algorithm, and the false alarm rate is reduced in combination with a double-track verification mechanism; and predicting a data trend by using the space-time diagram convolutional network, and outputting an interpretability analysis report through the generative large model. According to the invention, a multi-protocol adaptation engine and a distributed stream processing architecture are adopted, and a protocol analysis layer automatically identifies a plurality of industrial protocols, so that the manual adaptation time is reduced; and real-time synchronization of production line-level data is realized through edge node parallel acquisition and compression transmission.

The invention relates to the technical field of knowledge graph retrieval, in particular to a genuine medicinal material intelligent retrieval method and system based on a graph. The method comprises the following steps: performing semantic granularity analysis on a retrieval request input by a user, constructing a multi-level semantic edge and generating a hierarchical semantic graph structure; semantic enhancement is carried out on the map relation through semantic annotation, and a multi-condition intention is extracted in combination with dimensions such as regions, drug properties and channel tropism; further, the system executes multi-hop path combination, a structured semantic path conforming to the composite intention is mined, edge nodes in the path are inferred and complemented, and a genuine medicinal material retrieval result with a closed structure and complete semantics is generated. Compared with a traditional keyword matching and static field retrieval mode, the method has higher semantic perception ability and reasoning intelligence, and the accuracy and adaptability of the system in processing fuzzy, composite and path-incomplete retrieval scenes are remarkably improved.