36225 results about "Data transmission" patented technology

The invention discloses a smart park full life cycle management system and method based on digital twinning and Internet of Things, and relates to the technical field of smart park management, and the system comprises a sensing edge module, a data governance module, an intelligent analysis module, a life cycle module and a twinning modeling module. According to the invention, multi-protocol access and edge computing capability are supported, and the data transmission efficiency and stability are greatly improved; the intelligent analysis module outputs an accurate analysis result by constructing a multi-class feature matrix and deep multi-task joint modeling mechanism, and provides data support and model guidance for dynamic management and intelligent decision making of the park; the life cycle module integrates a Kepler optimization algorithm and a multi-agent reinforcement learning and simulated annealing algorithm, establishes a collaborative optimization mechanism, realizes combination of global search and local fine tuning of resource scheduling, and effectively optimizes energy consumption, response time, space utilization and safety risks; and the twin modeling module constructs a park three-dimensional model, so that the interactivity and operability of the system are improved.

The present invention relates to the technical field of data transmission. Disclosed is an application-driven three-dimensional spatial data transmission method. The method comprises: determining system key performance indicators (KPIs) by means of qualitative and quantitative analysis; constructing an AI-driven adaptive three-dimensional data transmission mechanism, and dynamically adjusting a transmission strategy on the basis of a real-time network state, a device capability, an application scenario and the KPIs; developing an adaptive compression algorithm set oriented to three-dimensional data, so as to meet differentiated compression requirements of different application scenarios; performing loop execution of a test, and adjusting and optimizing the data transmission mechanism and the compression algorithm set on the basis of a test feedback result and the real-time network state; and deploying an optimized transmission method to a production environment, and collecting field data to optimize the system performance and verify the achievement of the KPIs. By constructing a qualitative and quantitative analysis framework based on machine learning, the present invention quantifies differentiated transmission requirements of different application scenarios and formulates transmission strategies meeting the scenario requirements.

The present disclosure relates to a communication technique which combines a 5th generation (5G) communication system for supporting a higher data transmission rate beyond a 4G system with an internet of things (IoT) technique and a system thereof. The present disclosure may be applied to intelligent services (e.g., a smart home, a smart building, a smart city, a smart car or a connected car, health care, digital education, retail business, security and safety related services, etc.) based on 5G communication technology and IoT related technology. According to various embodiments of the present disclosure, in a wireless communication system, a terminal may include at least one transceiver and at least one processor coupled with the at least one transceiver, the at least one processor may be configured to receive system information from a cell of a base station, determine whether the cell supports new radio (NR) lite, based on the system information, and if the cell supports the NR lite, perform a random access procedure for the cell, and for the terminal performing the NR lite, at least one of a subcarrier spacing (SCS), a transport block (TB) size, or a bandwidth part (BWP) is reduced to a designated value, compared to other terminal not performing the NR lite.

A system for monitoring hemodynamic status of a patient can include a transducer, an adapter and one or more monitor devices. The adapter may be in communication with the transducer and the one or more monitor devices. The adapter can be configured to receive and process data from the transducer such as unprocessed physiological data. The adapter can be configured to transmit data to the monitor device(s) such as processed and / or unprocessed physiological data. The adapter can be configured to generate, and transmit to the monitor devices(s), user interface data for rendering interactive graphical user interfaces to display information such as physiological information relating to a hemodynamic status of the patient. The adapter can be configured to receive and process, from the monitor device(s) user commands or instructions to control an operation of the system or its components.

A system for AI-supported leak detection and localization in water distribution infrastructures, consisting of: a large number of distributed sensor nodes mounted along a water pipe, each sensor node comprising the following: a pressure sensor configured to measure local hydraulic pressure fluctuations within the pipeline with a resolution of at least 0.01 bar; a flow sensor configured to measure the volume flow within the pipeline with an accuracy of at least ±0.5% of the measured value; an acoustic sensor configured to detect vibration signatures caused by leaks in a frequency range between 50 Hz and 20 kHz; an embedded microcontroller with integrated analog-to-digital conversion circuitry for digitizing sensor outputs; a wireless communication module configured to transmit time-synchronized sensor data to a cloud-based processing platform; and a local energy subsystem with a rechargeable battery and an optional circuit for generating photovoltaic energy; the cloud-based processing platform includes an artificial intelligence engine that comprises the following: a data acquisition module configured to receive and decode the transmitted sensor data and to perform time alignment; a supervised learning module that is trained on historical data of flagged leaks and non-leaks to classify incoming sensor patterns; an unsupervised learning module configured to detect anomalies by modeling normal operating baselines of the pipeline; and a topology-aware localization module configured to determine leak coordinates using the topology of the pipeline network, modeling the propagation of hydraulic waves, and estimating the arrival time difference from multi-node acoustic detections; and wherein the system is configured to provide real-time leak alerts and georeferenced visualization via a remote monitoring interface.

The invention discloses an Internet of Things data transmission optimization system based on intelligent routing, and relates to the technical field of data transmission optimization. The heterogeneous network access module supports adaptive switching of multiple protocols such as 5G, NB-IoT and LoRa, and monitors link quality; intelligent routing decision selects an optimal path by means of deep reinforcement learning, and end-to-end delay is reduced; data distribution scheduling split data streams are transmitted in a multi-path parallel mode, and forward error correction is combined to guarantee reliability. The network state prediction uses an LSTM to predict bandwidth, and a route is adjusted in advance; the secure transmission integrates iris authentication and dynamic encryption. According to the invention, efficient, reliable and secure transmission is cooperatively realized, energy collection and dormancy strategies are combined, the equipment endurance is greatly prolonged, the network energy consumption is reduced, and the method is suitable for industrial control, telemedicine and other scenes with high requirements on real-time performance and reliability.

The invention relates to the technical field of integration of artificial intelligence security and network security, and discloses an AI dynamic security transmission system based on an SASE framework, which realizes security access control based on AI dynamic identity verification through an SASE integration access module, acquires and predicts network performance change in real time by using a network state sensing module, and transmits the network performance change to a network server. Equipment, environment and data content are subjected to multi-dimensional analysis by means of a security risk assessment module, a quantitative risk score is generated, and a transmission protocol, parameters and encryption strength are dynamically adjusted according to a network state and the risk score by means of a dynamic transmission optimization module and a self-adaptive encryption module; the problem that safety protection and transmission efficiency are difficult to cooperate in a traditional architecture is effectively solved, low-delay and high-reliability data transmission service can be provided for AI application in a complex network environment, meanwhile, self-adaptive dynamic protection of the whole data transmission process is achieved, and data safety is comprehensively guaranteed.

The invention discloses an unmanned aerial vehicle cluster intelligent cooperative control method, and the method comprises the steps: optimizing a network topology through heterogeneous unmanned aerial vehicle cluster dynamic networking and a dynamic clustering algorithm, and guaranteeing the reliability of a communication link; a layered hybrid decision architecture is designed to improve the task allocation rationality and the dynamic adaptability of the unmanned aerial vehicle cluster; a distributed control strategy network is trained by using a multi-agent near-end strategy optimization MA-PPO algorithm, and unmanned aerial vehicle cluster behavior collaboration is ensured in combination with space-time consistency constraint; an asynchronous incremental consensus protocol AICP is provided, the data transmission amount is reduced, and topology reconstruction is accelerated; real-time three-dimensional environment reconstruction and dynamic threat prediction are realized based on a neural radiation field NeRF technology; a lightweight anti-interference communication middleware is developed, and the instruction transmission stability is enhanced by adopting a space-time coding diversity technology. The method solves the problems of high delay of centralized control of the unmanned aerial vehicle cluster, poor convergence of a distributed algorithm and the like, and is suitable for high-dynamic task scenes such as urban street battle and complex terrain search.

The invention provides a data management method, device and equipment and a readable storage medium, and the method comprises the steps: obtaining key value cache data generated by a first type of computing power unit in a pre-filling stage, transmitting the key value cache data to a memory pool, and caching the key value cache data in the memory pool; and in response to a data acquisition demand of the second type of computing power unit in a decoding stage, transmitting the specified key value cache data cached in the memory pool to the second type of computing power unit according to the data acquisition demand. According to the technical scheme of the invention, the memory pool is arranged and the aggregated transmission channel is utilized, so that the transmission efficiency of the key value cache data between the computing power devices is effectively improved, the data transmission delay is reduced, and the network transmission protocol overhead is reduced, thereby improving the data interaction efficiency of the computing power devices in a pre-filling stage and a decoding stage, and improving the user experience. The utilization of computing power resources is optimized, and the throughput and performance of large model reasoning are improved.

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 relates to the technical field of welding deformation control, and discloses a multi-stage adjusting method and system for steel structure welding deformation control. The method comprises the steps that steel member parameters are measured, a welding deformation prediction model is established, and pre-deformation data are obtained; performing accurate pre-deformation processing according to the data; executing segmented welding and recording a temperature field and a deformation amount; performing local heat treatment on the high-stress area to obtain stress distribution; correcting a deformation area by using multi-point force; and performing precision detection and quality evaluation according to the corrected data to form a control file. According to the method, high-precision control over welding deformation of the complex steel structure is achieved through data transmission and feedback adjustment of all stages.

The invention relates to the technical field of data security management, and discloses a blockchain-based forestry data security management system and method, and the system comprises a data collection unit which is used for comprehensively obtaining a multi-dimensional information flow of forest ecology and resource states; the data processing unit is used for carrying out deep cleaning, standardized regulation and feature value extraction on the originally collected mass heterogeneous data; the block chain storage unit is used for constructing a permanent, tamper-proof and distributed secure storage infrastructure platform of the forestry core data; a verification unit; the safety control unit is used for constructing a covering data transmission, storage and access full-chain depth defense system structure model; a user interface unit; an auditing unit; and a network communication unit. The method is reasonable in design, and the intelligent analysis module is used for processing to form a forest interannual growth trend prediction function distribution map state visual output result set.

The invention relates to a network real-time synchronization communication system, and relates to the technical field of computer network communication. The system comprises four core modules: a hardware optical IO synchronization module which integrates a multi-wavelength optical transceiver array and an anti-jitter circuit and provides a nanosecond global clock signal, and the synchronization precision is less than or equal to 15ns; the time sequence arrangement module is used for dividing a fixed time window and a dynamic buffer window based on a time sensitive network, supporting a priority preemption mechanism of the SRIO bus and realizing deterministic data transmission; the distributed RTDATA nodes replace a traditional single-board computer, a heterogeneous computing unit and an intelligent storage controller are integrated, and protocol stack processing delay is reduced through the zero copy technology; according to the dynamic multicast routing system, a safely isolated multicast tree is constructed as required, and link load distribution is optimized in combination with machine learning. Through collaborative design of hardware optical synchronization and dynamic data arrangement, the problem of real-time performance reduction caused by multi-node communication concurrence is solved.

The invention provides an electronic information data association method and system based on the Internet of Things. The method comprises the steps that a parallel processing architecture is adopted to execute rapid protocol conversion on a high-priority data packet, data recombination is directly carried out through a pre-compiled conversion rule template, a complex format verification link is skipped, and the integrity and format correctness of the converted data packet are determined; response time and error rate statistical information in the data transmission process are obtained, protocol conversion rules and scheduling strategies are optimized through a feedback mechanism, and if the success rate of certain protocol conversion is lower than a standard value, corresponding mapping rules and verification algorithms are updated; and carrying out standardized packaging on all converted data by adopting a unified data exchange standard, adding an equipment identifier, a timestamp and a data type label, establishing a cross-protocol general data format, and carrying out final distribution after judging whether a data packet meets a standard format requirement.

A wireless communication method and system enabling dynamic multi-path data transmission and reception across heterogeneous radio access networks. The method includes selecting between sequential, concurrent, or redundant multipath transmission strategies based on real-time measurements of link quality parameters, including signal-to-interference-plus-noise ratio (SINR), reference signal received power (RSRP), delay, and spectrum availability. The system further enables adaptive transmission power control, modulation scheme selection, and carrier configuration based on physical layer feedback, minimizing interference and optimizing spectral efficiency. Peer-to-peer communication between terminals is supported without base station mediation, reducing RAN load. Multi-base station connectivity is enabled, allowing terminals to simultaneously transmit through multiple nodes and aggregate at the core network. The architecture supports real-time topology control, dynamic link adaptation, and transmission parameter adjustment based on physical-layer constraints.

The present disclosure relates to a communication technique and system for combining an IoT technology with a 5G communication system for supporting a higher data transfer rate than a 4G system. According to an embodiment of the present invention, a method of a terminal supporting subband non-overlapping full duplex (SBFD) in a wireless communication system may comprise the steps of: receiving time duplex division (TDD) resource configuration information and SBFD resource configuration information from a base station; determining respective indexes of first random access channel (RACH) occasions that are commonly valid in the TDD resource configuration information and the SBFD resource configuration information; and determining respective indexes of second RACH occasions that are invalid in the TDD resource configuration information and are valid in the SBFD resource configuration information.

The invention belongs to the technical field of aquaculture automation control, and particularly discloses an aquaculture environment intelligent disease early warning system and a storage medium, the system comprises a data acquisition layer, a data transmission layer, a processing platform layer and an application service layer; the data acquisition layer comprises an environment monitoring unit, a water quality sensor group, portable detection equipment and a data acquisition module; the data transmission layer comprises a wireless transmission module and an edge computing node; the processing platform layer comprises a machine learning prediction model and an environment factor correlation analysis model; the application service layer comprises an early warning information push module and a prevention and control scheme generation module. According to the intelligent disease early warning system for the aquaculture environment and the storage medium, a correlation model of environmental factors and disease occurrence is innovatively established, the early warning accuracy is 85% or above by adopting a machine learning technology, comprehensive monitoring and real-time early warning in the aquaculture process can be achieved, and accurate and efficient disease prevention and control are supported.

The invention discloses a data compression transmission method and system applied to a ferry inspection image, and the method comprises the steps: collecting and obtaining the ferry inspection image in real time, recognizing a key inspection target region in the image, and carrying out the segmentation and partitioning of the image; compressing the key inspection target area based on lossless compression coding; the quantization step size is dynamically adjusted by comparing statistical variances of background pixels between continuous frames, and lossy compression coding is carried out on a background area; based on the boundary distance between the key inspection target area and the background area, adaptive compression coding is carried out on the transition area; constructing a hierarchical data packet; and constructing a data transmission optimization model, dynamically allocating data transmission links, and obtaining a transmission scheme with the highest total transmission. The method has the advantages that efficient data compression transmission is realized by accurately segmenting the image area and adopting a lossless, lossy and adaptive compression technology, the overall transmission efficiency is improved through intelligent transmission optimization, and the definition and real-time performance of the inspection image are ensured.

The invention discloses a multi-welding-robot collaborative operation system based on digital twinning, and belongs to the technical field of intelligent manufacturing and robot control, the system comprises a physical space module, a virtual space module, a twinning data layer and a control module, and communication connection is established among the physical space module, the virtual space module, the twinning data layer and the control module; the physical space module comprises a plurality of welding robots, a sensor group and a data transmission network; the virtual space module comprises a multi-welding robot twinning body and a workpiece twinning model; the twin data layer is used for connecting the physical space module and the virtual space module; and the control module generates a welding path optimization scheme, a multi-welding-robot collaborative collision avoidance strategy and a welding quality feedback control instruction based on the twin data layer. According to the method, a closed-loop control framework of'physical-virtual 'bidirectional mapping is constructed, so that dynamic collaborative optimization of multi-robot motion trails, welding seam forming and thermal deformation is realized.

A computer-implemented method for operating an artificial intelligence voice agent system includes receiving voice input through communication channels; analyzing converted text through natural language processing (NLP) pipelines implementing intent recognition and sentiment analysis detecting emotional cues using a multimodal large language model (LLM); generating response content using machine learning models trained on domain-specific corpora; converting generated responses to synthetic speech through text-to-speech (TTS) engines; integrating with a customer relationship management (CRM) platforms or an enterprise resource planning (ERP) database; and implementing continuous learning by updating language understanding models using conversation logs, voice recognition parameters based on user feedback, and response generation patterns. One implementation is a computer-implemented system and method that operates a suite of intelligent interactive devices and platforms including an artificial intelligence voice agent, enhanced communication platforms, an intimacy companion system, and augmented / virtual reality eyeglasses. Further, one implementation includes AR / VR eyeglasses that project visual content onto interchangeable lenses or directly onto the user's retina via laser-based retinal projection, provide prescription adjustments, incorporate ear-mounted sensors for monitoring physiological parameters like heart rate, oxygen saturation, and blood pressure, and utilize wireless data transmission, onboard environmental sensing, and remote calibration, all designed to offer dynamically adaptive, secure, and context-aware interactions across communication, personal assistance, health monitoring, and immersive augmented or virtual reality environments.

The invention provides a complex scene-oriented AI large model lightweight deployment method, and relates to the technical field of edge computing, and the method comprises the steps: carrying out the structured pruning of a pre-trained Transform network based on the attention head importance score, carrying out the dynamic sparsification of the activation state of a feedforward network according to the input tensor entropy value, employing the dynamic mixing precision quantization, and carrying out the reconstruction of an AI large model. Obtaining network parameters after pruning quantization; deploying the pruned and quantized network parameters to an edge computing device, distributing a feature extraction operator to a neural network processor through a heterogeneous computing scheduler, and unloading a classification operator to a multi-core central processing unit; and managing an on-chip memory in combination with a virtual memory paging mechanism, realizing zero-copy data transmission by utilizing a direct memory access controller, and outputting a reasoning result tensor. According to the method, efficient and reliable operation of the large model at the resource-constrained edge node is realized.

The invention relates to the technical field of circuit detection, discloses a power transmission line thermochromic wire clamp heating early warning method and system, effectively solves the problem of data acquisition distortion in a strong electromagnetic environment, and improves the accuracy of state evaluation through multi-source data fusion. The dynamically adjusted early warning model reduces the risk of false alarm and missing alarm caused by equipment aging, the intelligent decision support module shortens the fault handling response time, the data closed-loop mechanism ensures the reliability of the system in the whole life cycle, and the reliability of the system in the whole life cycle is improved through multi-sensor cooperative monitoring and edge calculation processing. And the influence of environmental factors on data acquisition is reduced. The two-channel transmission architecture guarantees the data transmission integrity under different network conditions, the CRC verification mechanism effectively recognizes and corrects transmission errors, a high-quality data basis is provided for a subsequent early warning model, the abnormal data recollection mechanism avoids data missing caused by single collection failure, and continuous and stable operation of the monitoring system is ensured.

The invention relates to the technical field of satellite communication, in particular to a high-orbit and low-orbit satellite communication resource scheduling method and system, which collects real-time state information of high-orbit and low-orbit satellites, performs digital simulation of a satellite network based on the real-time information, constructs a satellite network management model and predicts satellite trajectory change. The method comprises the following steps: performing multi-dimensional evaluation on a satellite network management model by using a pre-constructed network evaluation framework, generating cross-orbit link evaluation information, analyzing parameters such as link quality and bandwidth, obtaining current service demand information, analyzing a communication resource allocation strategy in combination with a cross-orbit link evaluation result, and formulating a resource scheduling strategy according to strategy analysis. And a high-orbit and low-orbit satellite communication network is constructed through execution of a hierarchical network management mechanism and a cross-orbit data transmission protocol.

The invention relates to the technical field of fire early warning, and discloses a tunnel fire early warning method and system based on deep learning, and the method comprises the steps: deploying a sensor, constructing a wireless transmission network, and obtaining multi-modal data; carrying out preprocessing, feature extraction and feature fusion on the multi-modal data; constructing a tunnel fire early warning model adapted to the construction environment and designing a dynamic threshold mechanism; and designing a multi-level early warning response mechanism and carrying out early warning. According to the invention, the dust-proof smoke sensor is arranged to overcome the problem of susceptibility to dust interference; sampling frequency and communication frequency are automatically adjusted according to monitoring requirements during data transmission, so that the monitoring effect is ensured and the service life of a battery is prolonged; a tunnel fire early warning model based on TCNN is constructed, and a large-scale fire data set is constructed through FDS digital simulation to train the model; a dynamic threshold value capable of being automatically adjusted according to the environment is designed, and the problem that a fixed threshold value system does not adapt to the changing environment is solved.

The invention discloses a building engineering quality monitoring system, and relates to the technical field of building engineering monitoring, the building engineering quality monitoring system comprises a collection module, an analysis module, a monitoring module and an early warning module, the collection module collects first quality monitoring data of building engineering and obtains historical monitoring data, and transmits the data to the analysis module; performing feature extraction and data analysis on the first quality monitoring data to obtain second quality monitoring data, storing historical monitoring data and presetting quality standard data, transmitting the second quality monitoring data and the quality standard data to a monitoring module, performing dynamic comparison on the real-time second quality monitoring data and the preset quality standard data, and outputting the result. The method comprises the steps of generating quality anomaly feature parameters, transmitting the quality anomaly feature parameters to an early warning module, matching a preset early warning strategy according to the quality anomaly feature parameters, sending out graded early warning signals, and carrying out multi-dimensional early warning by integrating multi-dimensional data acquisition and analysis, dynamic feature index extraction and dynamic standard construction, so that the engineering quality monitoring accuracy and the management and control timeliness are improved.

The invention discloses a multi-source heterogeneous data synchronization system, and relates to the field of computer information management application. The system fuses key technologies such as flow dynamic regulation and control, self-adaptive load balancing and intelligent semantic mapping. Through a real-time network state monitoring and self-adaptive flow regulation and control algorithm, the data transmission rate is dynamically adjusted according to the server resource utilization rate, the data priority and the network bandwidth, data backlog and system overload are avoided, and the robustness and the real-time performance of a synchronization task are remarkably improved. The intelligent field mapping engine is based on semantic analysis, word vector representation and a machine learning model, automatically recognizes field semantic consistency of heterogeneous data sources, generates an optimal mapping rule and dynamically optimizes a mapping result, the manual configuration cost is reduced, and the mapping accuracy rate reaches 98% or above. The system has high expandability and self-learning ability, is suitable for a large-scale multi-source heterogeneous data integration scene, and provides an efficient, intelligent and self-adaptive full-stack solution for a data synchronization task.

According to the artificial intelligence-based data analysis system and method provided by the invention, a set of novel data analysis system architecture is designed, and key technical components such as natural language processing, a large language model, database query optimization, multi-modal visualization and a semantic knowledge graph are fused; data transmission between modules is realized through unified intermediate data objects such as a structured query intention, an analytic tree and a structured query language template object, and asynchronous collaboration is realized through event driving and a message queue mechanism, so that a non-professional user can input an analysis request through a natural language; semantic analysis, structured query language query statement generation, data query and result visualization presentation are automatically completed, the method can be widely applied to data analysis scenes in the industries of government affairs, traffic, finance, education and the like, the data use efficiency is improved, the technical threshold is reduced, and the digital decision-making ability is enhanced.

The invention discloses a network data intelligent tool system and method based on a model context protocol MCP, and relates to the technical field of computer networks. The system comprises an AI analysis main body, an MCP Pcap tool engine, context management, a Pcap data source, various MCP Pcap tools, a Pcap tool gateway interface, a Pcap tool interface and a data packet interface. The invention provides an interaction mechanism of direct embedding and bypass data forwarding in tool calling. According to the method, direct embedded transmission of small-size data in tool calling is supported, separation of data transmission and instruction calling is achieved, the technical bottleneck that large-size Pcap data cannot be efficiently exchanged through a text channel is effectively overcome, efficient processing of the AI model on network packet capture data is achieved, and the data transmission efficiency is improved. And the efficiency and the automation degree of large-scale network traffic analysis are improved.

The invention relates to the technical field of abnormal state prediction, provides a multi-dimensional time sequence equipment abnormal state prediction method and system, and solves the problems of high false alarm rate and risk prediction inaccuracy in the prior art. The method comprises the following steps: collecting a multi-node operation state data set and communication transmission layer time sequence offset information of an equipment distributed system; jitter and delay association processing is carried out on the data, and a communication stability feature set is generated by quantizing space-time correlation of a jitter extreme value and delay fluctuation; obtaining physical layer deformation and temperature drift data of the communication cable, analyzing and generating a physical layer disturbance feature sequence based on optical signal feature offset, and converting the physical layer disturbance feature sequence into channel abnormal strength features; and cross-level feature coupling is carried out on the communication stability feature and the channel abnormal strength feature, and a communication node failure or data transmission abnormal risk is predicted based on a coupling result. According to the invention, millisecond-level linkage prediction of equipment-level physical disturbance and system-level communication risks is realized, the fault false alarm rate is reduced, and the cascade failure risk is blocked.

The invention provides an energy storage cabin terminal remote upgrading method and system based on communication protocol optimization, and relates to the technical field of digital information transmission, and the method comprises the steps: deploying a bandwidth detection mechanism, adjusting a self-adaptive block transmission strategy, and dividing upgrading firmware into M dynamic data packets; constructing a dual-path transmission layer, and configuring a dual-path redundancy check mechanism; a missing block query rule is adopted, and a local flash memory is used for caching Hash check values of the received N dynamic data packets; analyzing a mapping relation between the block index and a multi-path redundancy check mechanism, and establishing a check mapping table; and performing real-time verification and state marking, and configuring a remote upgrading process to execute the upgrading operation of the energy storage cabin terminal equipment after incremental recombination is performed by comparing and verifying abnormal data packets. According to the invention, the technical problem of insufficient reliability of data transmission caused by lack of adaptability to a dynamic network environment due to the fact that most of energy storage cabin remote upgrading technologies in the prior art depend on a single communication path to carry out data transmission is solved.