1521 results about "Real time transmission" patented technology

The invention provides a real-time transmission method and system based on internet-of-things sensing data in a digital twinborn scene, and the method comprises the steps: collecting and processing vibration spectrum data, pressure gradient change data and temperature distribution data of equipment in an industrial production line, generating a preprocessing data package, carrying out the analysis to obtain an equipment state fingerprint feature and an environment working condition label, and carrying out the real-time transmission of the environment working condition label. A target virtual control parameter and a target error compensation amount are generated by using a dynamic parameter correction loop of the digital twin, an actual material flow state is generated in combination with material flow rate data, an optimization strategy sequence of multi-device collaborative operation is generated by using a distributed computing architecture, and the optimization strategy sequence is converted into a track compensation parameter of an end effector in the device; in combination with the target error compensation amount, data closed-loop transmission is carried out; according to the invention, millisecond-level time domain matching of vibration, pressure and temperature data and material flow velocity is realized through a dynamic timestamp alignment mechanism of 5G edge computing nodes, and the problem of synchronization error accumulation of traditional internet-of-things sensing data is solved.

The invention discloses a construction site safety risk intelligent early warning system and method based on BIM and big data analysis, relates to the technical field of building engineering construction safety, and solves the problem that it is difficult to transmit construction site multi-source data which is collected and preprocessed in real time in real time and carry out space mapping with a BIM model. A rule engine is difficult to carry out initial early warning; a machine learning model is difficult to analyze time series data, predict collapse risks and identify dangerous behaviors; a risk prediction model is difficult to construct and is difficult to integrate into a BIM model; and pushing and closed-loop management are difficult to carry out on the risk early warning information. According to the method, the multi-source data is collected at the construction site, the digital twinborn scene is constructed by mapping the multi-source data to the BIM model by means of space-time alignment, the multi-source data is analyzed and processed by applying technologies such as a rule engine and a machine learning algorithm, and the result is integrated to the BIM model, so that visual risk monitoring and early warning are realized.

The invention provides a patrol route self-adjusting method and system based on unmanned aerial vehicle patrol, and the method comprises the steps: firstly obtaining a multi-source environment perception data set of a target area, then carrying out the feature extraction of the multi-source environment perception data set, inputting a dynamic route planning model, generating an obstacle avoidance correction vector set and an environment adaptation parameter set, and carrying out the feature extraction of the multi-source environment perception data set; and carrying out spatial position offset compensation on the reference route based on the obstacle avoidance correction vector set to generate an initial correction path node set, carrying out path smoothness optimization processing on the initial correction path node set according to the environment adaptation parameter set to generate a final dynamic patrol route, and finally, carrying out dynamic patrol. The dynamic patrol route is converted into a waypoint control instruction stream which can be executed by the unmanned aerial vehicle flight control system, and the waypoint control instruction stream is transmitted to the airborne controller in real time to drive the unmanned aerial vehicle to execute the patrol task, so that dynamic self-adjustment of the unmanned aerial vehicle patrol route is realized, and the patrol flexibility and safety are improved.

The invention relates to the technical field of computer network security and data transmission, and discloses a computer network security data transmission method and device, and the method comprises the steps: obtaining real-time threat intelligence data, a real-time packet loss rate and a real-time transmission performance parameter, carrying out the encryption, and obtaining encrypted fragmented data; inputting the real-time transmission performance parameters into a path performance evaluation model to obtain a path performance score; sorting according to the path performance score to obtain a transmission path set; according to the transmission path set, matching to obtain a sequence corresponding relation; generating complete transmission data according to the sequence corresponding relation; performing anti-attack optimization according to the real-time transmission performance parameters to obtain a link connection strategy; analyzing the link quality according to the real-time packet loss rate to obtain a transmission link quality score; and performing dynamic adjustment according to the transmission link quality score, the link connection strategy and the complete transmission data to obtain real-time transmission data. According to the method, real-time and complete computer network security data transmission can be realized.

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 discloses a coal mine heat energy monitoring and cooling regulation and control method based on the Internet of Things, and relates to the technical field of coal mine heat energy monitoring and cooling regulation and control. The collected temperature data are transmitted to a central monitoring system in real time through an Internet of Things gateway; redundant temperature sensors are arranged at key positions, and data of the temperature sensors in the same area are regularly compared and verified. According to the invention, the monitoring robustness is improved through a redundant temperature sensor and a data fusion algorithm, a monitoring blind area caused by a single-point fault is avoided, and the accident risk is reduced; a self-adaptive learning algorithm is adopted to dynamically optimize a detection threshold value and a compensation strategy, so that the anomaly detection accuracy is improved, and false alarms and missing alarms are reduced; the temperature trend is pre-judged through a predictive control algorithm, cooling is conducted in advance, balance between safety and energy efficiency is achieved, overheating accidents are prevented, and energy consumption is reduced.

The invention relates to a multi-channel signal integrity optimization method and system for a passive copper cable, and the method comprises the steps: obtaining channel characteristic parameters and signal transmission requirements of the passive copper cable, and carrying out the impedance matching, and obtaining an optimized transmission channel; acquiring real-time transmission data of the passive copper cable, and performing equalization processing on the real-time transmission data through the optimized transmission channel to obtain a stable transmission signal; acquiring crosstalk interference data acquired by the passive copper cable, and carrying out shielding analysis on the crosstalk interference data and the optimized transmission channel to obtain a crosstalk suppression measure; performing frequency characteristic analysis on the crosstalk interference data to obtain frequency compensation information, and performing transmission prediction on the frequency compensation information based on the crosstalk suppression measure to obtain a frequency compensation parameter; and performing multi-channel optimization on the initial signal optimization result according to the frequency compensation parameter to obtain a multi-channel optimization signal. According to the invention, complex and changeable transmission environments can be coped, and stable transmission of signals is ensured.

The invention relates to the field of wind power plants, in particular to a wind power plant edge calculation data cleaning and real-time transmission optimization method and system. The method comprises the following steps: forming a multi-source heterogeneous data stream by collecting SCADA operation data, CMS vibration monitoring data and meteorological environment data of a wind turbine generator in real time; performing standardization processing on the data at an edge computing node, constructing a multi-dimensional feature vector, and generating a fusion data set by adopting a self-adaptive fusion algorithm based on a dynamic weight; and constructing a fault prediction model based on the deep dual-channel convolutional neural network, and outputting a health state evaluation value and a fault risk level in real time. And when the risk level exceeds a threshold value, generating an early warning signal. The system also performs data compression transmission optimization according to the real-time bandwidth state, and dynamically adjusts the weight of the monitoring parameter to realize the closed-loop optimization of the preventive maintenance strategy.

The invention discloses a device for measuring flowability of explosive modeling powder, which comprises a step-press-loading analogue unit, a timing and weighing unit and a computer control unit, wherein the step-press-loading analogue unit directly and indirectly drives a screw rod which extends into a blanking bin to rotate and perform reciprocating movements along an axial line and drives a pusher dog in the blanking bin to perform the reciprocating movements along an arc section through the combined action of a cylinder and a pneumatic reversal valve so as to ensure that the explosive modeling powder in the blanking bin falls into the timing and weighing unit, and the process more truly imitates an explosive conveying process of an explosive step-press process. The timing and weighing unit transmits a weighing value of the explosive modeling powder which falls in the timing and weighing unit to the computer control unit in real time, and the computer control unit calculates the instantaneous flow speed of the explosive modeling powder of a north side and generates a flow speed-time curve according to sampling data. The device can measure the flowability of the explosive modeling powder so as to provide an objective basis for choosing a proper explosive step-press process for the explosive modeling powder.

The invention relates to the technical field of aviation communication, and discloses a true north calibration system and method for an aviation airborne laser communication equipment alignment system, and the method comprises the steps: synchronously collecting flight data through an IMU sensor and a double-antenna GPS at a preset sampling frequency, and outputting the real-time attitude angle information of an aircraft through an IMU; filtering and de-noising the synchronously acquired IMU and double-antenna GPS data, and carrying out noise suppression and deviation compensation on accelerometer and gyroscope data output by the IMU by adopting a Kalman filtering algorithm; based on the preprocessed sensor data, calculating an initial course angle by measuring relative position change and phase difference between antennas by using a double-antenna GPS (Global Positioning System); the calibration controller transmits the calculated true north direction information to an alignment system of the laser communication equipment in real time through a communication interface; and the attitude change and external environment disturbance of the aircraft are continuously monitored in the whole flight process. The method has the advantage that the alignment performance and the communication stability of the laser communication equipment are improved.

The invention discloses a concrete strength remote monitoring method and system suitable for a complex environment, and belongs to the technical field of civil engineering structure health monitoring. The remote monitoring method comprises the following steps: step 1, acquiring performance data of a concrete structure and related environmental factor data, and transmitting multi-source heterogeneous data to a data processing center in real time through a preset wireless communication protocol; step 2, constructing a five-dimensional tensor data structure, and realizing accurate mathematical expression of a complex coupling relationship between environmental factors and material characteristics through tensor decomposition; step 3, capturing nonlinear time-varying characteristics of concrete strength evolution; 4, quantifying the age effect through an intensity development rate index; step 5, based on the intensity development rate change trend, adaptively adjusting the data sampling frequency and monitoring the environmental condition fluctuation; and step 6, evaluating the safety state of the concrete structure in real time, and ensuring safe and reliable operation of the concrete structure in a complex environment.

The invention discloses an intelligent water quality monitoring device based on Internet of Things, relates to the technical field of environmental monitoring, and aims to analyze a complex nonlinear relationship in water quality data, accurately predict a water quality change trend and identify potential water quality problems in advance through a deep learning model and a system. The dynamic weight calculation module can dynamically adjust the importance of water quality parameters according to real-time data and environmental changes, the monitoring precision and adaptability are improved, an intelligent feedback mechanism automatically adjusts the sampling frequency and the sensor position when an early warning signal is triggered, accurate monitoring of the water quality changes is ensured, and the monitoring accuracy is improved. The Internet of Things technology supports real-time transmission of water quality data and analysis results to the cloud, managers can check water quality states, historical trends and early warning records through a remote monitoring interface, the response capability and management efficiency of the system are improved, and the intelligent water quality monitoring device has the advantages of being high in precision, high in intelligence and high in adaptability.

The invention relates to the technical field of operation and maintenance management, in particular to an operation and maintenance management method based on digital twinning, and the method comprises the following steps: S1, building a corresponding digital twinning model through collecting the structure data and operation data of an actual operation and maintenance system; s2, collecting operation data of the operation and maintenance system in real time, transmitting the operation data to the cloud platform, and preprocessing the operation data transmitted in real time in the cloud platform; s3, performing real-time analysis on the preprocessed data through a digital twinborn model, and detecting a system operation state and an abnormal condition; s4, based on the historical data and the real-time data, using a machine learning algorithm to construct a fault prediction model, and predicting possible faults in the future; and S5, generating an optimization management scheme according to the fault prediction result and the system operation state. Through accurate data acquisition and real-time monitoring, potential faults and hidden dangers of equipment can be found in time, accidental shutdown of the equipment is avoided, and production loss caused by faults is greatly reduced.

The invention discloses a dam termite cave positioning method based on transient electromagnetism carried by an unmanned aerial vehicle. The method comprises the following steps: constructing an unmanned aerial vehicle transient electromagnetic detection system; preprocessing the received secondary induction electromagnetic field signal, and obtaining a comprehensive electromagnetic signal according to the response difference of the multi-frequency signal; inputting the integrated electromagnetic signals into a pre-trained deep learning neural network model, identifying characteristic signals related to termite acupoints in the integrated electromagnetic signals, and outputting position coordinates of suspected termite acupoints; a dam surface thermal imaging image obtained by a thermal imaging module is combined for secondary verification, and termite cave position information is transmitted to a ground control terminal in real time through a communication module carried by the unmanned aerial vehicle. According to the invention, efficient, accurate and lossless positioning of the termite cave of the dam is realized, underground electromagnetic signals and surface heat distribution information of the dam are rapidly obtained, and accurate termite cave position coordinates are output.

The invention discloses a security and protection monitoring video real-time transmission method based on the Internet of Things, and relates to the technical field of security and protection monitoring video transmission, and the method specifically comprises the following steps: when a texture repetition region exists in an image frame, determining all macro blocks forming the texture repetition region in the image frame, and marking the macro blocks as texture repetition blocks; performing comprehensive analysis on each texture repetition block, and evaluating a candidate motion vector direction dispersion degree when an image frame in the security and protection monitoring video has a texture repetition area; based on the evaluation result, determining whether to classify each texture repetition block and whether to respectively match a corresponding motion vector selection strategy; and according to the matched motion vector selection strategy, respectively executing corresponding dynamic regulation and control operations. According to the method, the problem that the direction of the motion vector is discrete and uncontrollable in a texture repetition area is solved, dynamic regulation and control of a coding strategy and synchronous optimization of a decoding end are realized, and the image reconstruction precision and the intelligent analysis stability are improved.

The invention relates to the technical field of audio transmission systems, and provides a noise interference compensation method applied to an audio transmission system, and the method comprises the steps: firstly obtaining a noise-containing audio signal set of a target audio transmission channel, the noise-containing audio signal set comprises a plurality of noise-containing audio segments and pure audio segments corresponding to the noise-containing audio segments; then noise interference feature extraction is carried out on the noise frequency signal set to obtain a time domain noise feature and a frequency domain noise feature of each noise frequency segment, and then dynamic feature fusion is carried out on the time domain noise feature and the frequency domain noise feature based on a preset noise compensation model to generate a noise interference feature set; the method comprises the following steps: acquiring a noise interference feature set, training a noise compensation model according to a mapping relation between the noise interference feature set and a corresponding pure audio clip, generating a noise compensation parameter set, finally loading the noise compensation parameter set to a real-time processing module, and performing noise interference compensation operation on a noise-containing audio signal transmitted in real time. Therefore, the influence of noise on audio signals is effectively reduced, and the audio transmission quality is improved.

The invention discloses an intelligent auxiliary fracture monitoring system for orthopedic nursing, which comprises the following steps that: the system acquires mechanical parameters, movement track data and biological signals of a fracture patient through a multi-mode sensor, and intelligently analyzes the data by using a deep learning algorithm; generating a fracture state diagnosis result and recovery trend prediction data; the low-power-consumption wireless communication module realizes real-time transmission of diagnosis data and supports remote system parameter adjustment; the personalized nursing scheme generation module generates a rehabilitation plan, a load adjustment strategy and an auxiliary device configuration scheme through an optimization algorithm based on the patient biomechanical model and the diagnosis result; and the adaptive feedback adjustment module dynamically adjusts a data acquisition strategy and processing parameters to ensure the real-time response capability of the system to the state of the patient. The system is suitable for the technical field of intelligent auxiliary monitoring, and can realize real-time monitoring, intelligent diagnosis and personalized rehabilitation guidance of fracture patients.

The invention relates to the technical field of equipment collaborative operation of the Internet of Things, in particular to a transit hub equipment collaborative operation method and system based on the Internet of Things technology. According to the method, original attitude motion data of a target ship are collected in real time through an attitude sensor network of the Internet of Things, and a real-time attitude drift vector of the target ship relative to a geodetic coordinate system is calculated; constructing a dynamic coordinate transformation matrix according to the real-time attitude drift vector; acquiring an expected operation track control instruction preset by the quay crane control system according to the geodetic coordinate system; performing coordinate transformation operation on the expected operation track control instruction through a dynamic coordinate transformation matrix to obtain a compensated cooperative control instruction; the compensated cooperative control instruction is transmitted to a quay crane control system in real time through an Internet of Things communication link, and a quay crane lifting appliance is driven by the quay crane control system to conduct lifting operation according to the compensated track instruction; and the safety and efficiency of the hoisting operation between the ship and the quay crane can be effectively improved.

The invention discloses a specific detection value identification method and device and a computer program product, and the method comprises the steps: S1, collecting the power data of a to-be-detected node of a power system, and transmitting the power data to an edge calculation layer in real time through a distributed operation system; s2, preprocessing the power data at an edge calculation layer to generate standardized time sequence data; s3, extracting time domain statistical features and frequency domain transformation features of the standardized time series data, and fusing high-dimensional features output by a pre-training deep learning model to construct a multi-dimensional feature vector; s4, performing pattern recognition on the multi-dimensional feature vector based on a pre-trained classification or clustering model, and outputting an anomaly detection result; and S5, feeding back an abnormal detection result to the power equipment control unit in real time through the distributed operating system, and triggering an alarm or executing parameter adjustment. According to the invention, efficient distribution of detection results and multi-node parallel processing can be realized, and the system performance is further optimized.

The invention relates to the technical field of vehicle-mounted communication, and discloses a CAN network-based vehicle-mounted equipment data transmission and interaction method, which comprises the following steps of: S101, acquiring a message sent by vehicle-mounted equipment, and analyzing to extract vehicle operation data; s102, receiving the vehicle operation data by the CAN gateway controller, extracting signal content, performing classification optimization, and performing compression packaging; s103, sending the packaged vehicle operation data to an external terminal device; s104, the external terminal device receives and analyzes the packaged vehicle operation data; and S105, the remote terminal sends an encrypted control instruction to the vehicle-mounted gateway, the gateway verifies the encrypted control instruction, converts the encrypted control instruction into a CAN message and executes the CAN message, the ECU generates a feedback signal and returns the feedback signal, and the gateway feeds the feedback signal back to the remote terminal through wireless communication to complete closed-loop interaction. According to the invention, the real-time transmission of safety key data is ensured, and the stability of wireless communication is improved.

The invention relates to the technical field of substation operation and maintenance, particularly provides a substation partial discharge detection method and system, and aims to improve the precision of partial discharge detection and the intelligent level of equipment insulation state assessment. The partial discharge signal data set comprises a plurality of monitoring signal sequences composed of discharge pulse waveforms, then performing feature extraction on the partial discharge signal data set, generating a time domain and frequency domain feature set of each monitoring signal sequence to comprehensively reflect the intrinsic characteristics of the discharge signals, and then utilizing a preset discharge mode analysis model to analyze the partial discharge signals. And performing discharge mode analysis on the time domain and frequency domain feature set, accurately identifying discharge types, and finally, generating an equipment insulation state evaluation strategy according to a discharge type classification result, and transmitting the equipment insulation state evaluation strategy to a substation monitoring system in real time to trigger early warning operation, thereby effectively ensuring safe and stable operation of a power system.

The invention discloses an intelligent optimization system based on a self-adaptive control hydraulic cylinder, and relates to the technical field of hydraulic cylinder control, and the system comprises a monitoring data collection module which collects data of a flow field, a temperature field and a stress field in real time by means of various sensors arranged at different parts of the hydraulic cylinder, synchronously carries out the preprocessing and coding of the collected data, and transmits the data to a data processing module; the data is transmitted to the data processing and analyzing module in real time; detailed data of the hydraulic cylinder in different physical fields are acquired in real time through the monitoring data acquisition module, and the coupling coefficient is accurately calculated by using the multi-physical field coupling model, so that the system can deeply understand the specific influence of each physical field on the performance of the hydraulic cylinder; according to the method, the system can construct a more accurate hydraulic cylinder dynamic model in the model prediction control module, so that the future state of the hydraulic cylinder is accurately predicted, further, the system can obtain an optimal control input sequence through an objective function optimization algorithm, and the pertinence and effectiveness of a control strategy are remarkably improved.

The invention discloses a hydraulic water quality monitoring control system for a drainage pipe network, which belongs to the field of data communication of a data center and comprises a rain gauge, a flow meter, a liquid level meter and a water quality monitor. The system also comprises an intelligent data analysis and diagnosis module, a remote control and automatic management module, an environment adaptability and safety protection design, a communication and data transmission optimization module, and an intelligent fault self-diagnosis and maintenance prompt module. A data analysis algorithm is adopted, data are transmitted to a central monitoring platform in real time through a wireless communication module, and centralized processing and analysis of the data are achieved. Meanwhile, the system has a remote control function and supports remote operation of drainage pipe network equipment through the intelligent tail end power supply controller. The environment adaptability design ensures that the equipment can stably operate in a severe environment, and the communication and data transmission optimization ensures the real-time performance and accuracy of data.

The invention discloses an eVTOL multi-camera cooperative video compression coding method based on multi-source perception and intelligent partitioning. The method comprises the following steps: constructing a scene three-dimensional perception model by fusing multi-source data of visible light, infrared and depth sensors; the method comprises the following steps: realizing dynamic video partitioning based on motion vectors and semantic analysis, and dividing a picture into a core region, a secondary region and a background region; establishing a parallax compensation motion prediction model by adopting a cross-camera reference frame sharing mechanism; and high-fidelity compression of the key area is realized through layered entropy coding and a dynamic quantization parameter distribution strategy. And the decoding end reversely executes multi-source data fusion and partition reconstruction according to the coding metadata. The method is suitable for eVTOL multi-camera video real-time transmission scenes such as polling, surveying and mapping.

The invention relates to the technical field of intelligent high-speed vehicle passing control, and provides a method for predicting a vehicle driving track under highway passing control based on digital twin tracking simulation, and the method comprises the steps: collecting highway digital twin system, target vehicle and environment data in real time, and carrying out the integration to form an input data set; a future driving track of a vehicle is predicted by using a pre-trained LSTM deep neural network model, and an actual track is collected through devices such as a GPS and the like for verification. And when the prediction deviation exceeds the preset range, the model is dynamically adjusted to improve the accuracy. Prediction results and deviation analysis are transmitted to a background control terminal and a driver mobile terminal in real time, and traffic control, accident early warning and driving aid decision making such as front road condition prediction and suggested driving speed providing are supported. The highway traffic efficiency and safety are effectively improved, the driver experience is enhanced, the development of an intelligent traffic system is promoted, the operation cost is reduced, and remarkable social and economic benefits are achieved.

The invention discloses a geological disaster monitoring system and method based on iron tower big data, and relates to the technical field of geological disaster monitoring. Comprising an iron tower monitoring node module, an edge calculation module, a data transmission module, a cloud big data processing and analysis module, a visualization and early warning issuing module and an emergency response and decision support module. According to the invention, communication iron tower resources are provided and a plurality of sensing devices are arranged, so that low-cost and large-scale deployment is realized, the monitoring coverage range is expanded, and the accuracy and reliability of geological disaster monitoring are improved at the same time; local preprocessing is carried out on the collected data through the edge computing module, and the data are transmitted to a cloud end in real time through a 4G / 5G network, so that real-time monitoring and quick response of the data are realized, and the timeliness of the monitored data is greatly improved; an intelligent analysis early warning system is established through a cloud big data processing and analysis module, and machine learning and an artificial intelligence algorithm are integrated to carry out multi-source data analysis and risk assessment, so that the early warning accuracy is improved.

The invention provides a low-delay streaming voice interaction system with an interruption processing function, and relates to the technical field of artificial intelligence. Necessary preprocessing and acoustic feature extraction are performed through a real-time acoustic processing module, and distortion and complex environmental noise introduced by an interaction channel are resisted through a robustness enhancement technology; the streaming acoustic decoding module performs acoustic modeling, language model application and decoding in real time in parallel, and outputs an ultra-low-delay text transfer result stream; the real-time acoustic processing module is combined with a signal processing technology and is responsible for detecting user voice activity in a high-precision and ultra-low-delay manner, and particularly judging the real-time voice activity state of a user through the user voice activity in an AI voice playing period; an efficient and low-delay bidirectional streaming network transmission mode is adopted between modules of the system and between the modules and a communication platform, and it is ensured that audio streams, acoustic feature streams, text streams and control signals can be transmitted and processed in real time with extremely low end-to-end delay.

The invention provides a rock-soil body multi-field coupling intelligent monitoring system comprising a multi-field sensing device configured to monitor parameters of a deformation field, a seepage field, a temperature field and a stress field of a rock-soil body; the edge calculation and transmission device is configured to perform preprocessing and real-time transmission on the multi-field sensing data; and the intelligent decision-making device is configured to analyze the preprocessed data based on a multi-field coupling model and generate disaster early warning information. According to the rock-soil body multi-field coupling monitoring system and method provided by the invention, synchronous monitoring of a deformation field, a seepage field, a temperature field and a stress field is realized through the multi-field sensing device; real-time data processing is performed in combination with an edge calculation and transmission device, and a dynamic early warning mechanism is established by using an intelligent decision-making device, so that the technical problems of multi-field data space-time correlation failure, insufficient multi-source data fusion precision and poor early warning mechanism adaptability are effectively solved, and the geological disaster early warning method has the remarkable advantages of improving the geological disaster early warning accuracy and timeliness.

The invention discloses a material transmission control adjustment method and system based on the Internet of Things, particularly relates to the technical field of material transmission control, and is characterized in that the weight of a sensor is dynamically adjusted based on a Kalman filtering algorithm, and the positioning precision of a material barrel is optimized; identifying and positioning an abnormal risk area and predicting a high-risk time period based on spatial-temporal feature analysis of historical data; optimizing a transmission path and speed by combining a digital twinborn model and a path planning algorithm; dynamically adjusting a resource scheduling strategy and calculating an optimal intervention path according to an abnormal prediction result and a path planning parameter; the conveying state of the material barrel is monitored in real time, and the safety and efficiency of the conveying process are ensured; the fault probability calculation module dynamically evaluates the fault probability based on the real-time transmission state; accurate control and dynamic adjustment in the material conveying process are achieved, and the problems that in a traditional material conveying system, positioning precision is low, abnormal prediction is not timely, and path planning is not optimized are solved.

The invention relates to the technical field of intelligent power distribution, in particular to an optical storage and charging integrated system based on a distributed soft bus, which comprises hardware equipment: a photovoltaic module, a wind driven generator, an energy storage module, a charging pile, acquisition equipment, a photovoltaic controller, a DCDC module and a mains supply / thermoelectric generator; communication and control: a distributed soft bus, a time sensitive network (TSN) and an intelligent control unit (EMS); according to the scheme, photovoltaic, wind power, energy storage, charging piles and other hardware equipment are connected through a distributed soft bus, real-time transmission of control instructions is achieved through a TSN, data are analyzed through intelligent algorithms such as a charging and discharging model and a multi-task learning model, an optimization strategy is generated, multi-energy cooperative control (including photovoltaic direct supply and energy recovery in the case of faults) is executed through an EMS, and the energy efficiency is improved. A user can set priorities through a terminal to participate in scheduling, and finally model parameters are corrected based on feedback data to form a light storage and charging integrated system of data acquisition, prediction optimization, intelligent control, user interaction and closed-loop evolution.