5938 results about "Nerve network" patented technology

The invention relates to the technical field of welding defect identification, and discloses a welding defect identification method and system based on a molten pool image. According to the method, a multispectral high-speed camera is used for collecting a molten pool dynamic image sequence in the welding process, after multi-scale morphological filtering preprocessing is conducted, a fusion feature vector is extracted through a depth separable convolutional network, and then the fusion feature vector is input into a defect classification model adopting a heterogeneous graph neural network architecture to obtain a defect probability distribution matrix. Then, constructing a dynamic sparse optimization model to position defects, generating a defect space coordinate set, and finally, outputting welding defect types and position information through hierarchical verification framework processing. According to the method, the problems of welding image noise interference, complex defect characteristics and the like are effectively solved, the accuracy and reliability of welding defect identification are improved, and powerful technical support is provided for welding quality control.

The invention relates to the technical field of data processing, in particular to a distributed multi-source heterogeneous sensor data processing method and system. The method comprises the following steps: collecting environmental parameters of a leakage area in real time through a distributed sensor array; performing coordinate system unification and timestamp alignment on the environmental parameters of the leakage area to generate a standardized leakage situation data set; extracting gas diffusion characteristics and thermal imaging characteristics of the standardized leakage situation data set to calculate a gas cloud cluster diffusion path probability; performing risk decision instruction generation on the gas cloud cluster diffusion path probability based on a preset leakage level classification neural network to obtain a risk decision instruction; calling a matched emergency plan based on the risk decision instruction; and analyzing the implementation steps of the emergency plan and performing instruction conversion to generate an emergency plan instruction. According to the invention, through real-time data acquisition, intelligent risk assessment and automatic emergency response, the timeliness responsiveness of data processing is improved.

The invention discloses a partial discharge detection method and device, and the method comprises the steps: outputting a multi-mode signal matrix after noise reduction through employing a self-adaptive noise reduction algorithm according to a multi-mode sensing signal during the operation of a generator; based on the multi-modal signal matrix, a space-time convolutional neural network is adopted to extract discharge features, meanwhile, a topological relation between signal time-frequency features and sensor space is captured, and a multi-modal feature fusion tensor is output; according to the multi-modal feature fusion tensor, a model is generated through a dynamic map, manifold learning and a particle swarm optimization algorithm are combined, and a dynamic fault map containing discharge intensity, phase and frequency point distribution characteristics is output; and based on the dynamic fault map, calculating a real-time discharge danger coefficient by using a risk prediction model, and outputting a discharge grading early warning instruction and a maintenance priority sequence. According to the embodiment of the invention, high-precision and traceable discharge detection and graded early warning can be realized.

The invention relates to the technical field of signal devices, and discloses a traffic signal control method and system based on vehicle-road cloud multi-modal data fusion, and the method comprises the steps: collecting multi-modal traffic data synchronously in real time through a vehicle-end sensor, road-side sensing equipment and a cloud Internet platform; fusing the heterogeneous data by adopting a space-time alignment algorithm, and constructing a standardized space-time feature matrix; traffic flow prediction is carried out based on a multi-layer space-time diagram neural network trained by a federated learning mechanism, and a signal control instruction is generated through reinforcement learning and a multi-objective optimization model; and issuing the green wave parameter, the dynamic timing scheme and the cross-domain coordination strategy to a roadside signal machine through the cloud edge coordination architecture to execute control. The problems that in the prior art, low-delay private network communication cannot be achieved, the data fusion efficiency is low, unmanned driving is not supported, and the deployment cost is high are solved, and the purposes of low-delay communication, high reliability and low risk are achieved.

The invention relates to the technical field of mobile energy storage vehicles, and discloses an AI-based mobile energy storage vehicle running state monitoring and analysis system which comprises a main controller, an AI monitoring coprocessor and a state analysis coprocessor. The main controller calls a monitoring analysis instruction and sends the monitoring analysis instruction to the state analysis coprocessor; an AI monitoring coprocessor dispatches a state analysis instruction and processes multi-modal data; and the state analysis coprocessor analyzes the parameters to generate a reference monitoring signal, cooperatively monitors the battery temperature, the charging and discharging efficiency and the load fluctuation in real time, and generates an abnormal correction signal and a state control instruction in combination with an analysis optimization mode. The system realizes accurate analysis of multi-source data and dynamic control of the energy storage unit through data preprocessing, feature dimension reduction and deep neural network prediction, improves monitoring accuracy, dynamic adaptability and intelligent level, and ensures safe and efficient operation of the mobile energy storage vehicle.

The invention belongs to the technical field of power distribution network voltage regulation and control, and discloses a distributed photovoltaic complex power prediction-cluster-cooperation-based power distribution network voltage regulation and control method, which integrates photovoltaic historical data, inputs an improved back propagation neural network model and outputs predicted photovoltaic active power output. Estimating the reactive capacity boundary of each node in real time based on the running state of the network-following inverter; dividing a distributed photovoltaic cluster by establishing a two-dimensional modularity function of a net load index and an equivalent electrical distance; a multi-device differential cooperative control strategy is provided for the voltage out-of-limit risk in the cluster; and constructing an optimization function with minimum network loss and voltage offset as a target, and optimizing and solving the function by using an improved multi-organization particle swarm optimization algorithm to obtain a multi-device adjustment sequence and a device action amount. According to the method, the renewable energy consumption capacity is improved and the network loss is reduced while the voltage stability of the power distribution network is ensured, and the comprehensive adjustment cost is optimized.

The invention relates to the field of power equipment, and discloses a power equipment fault early warning system, which comprises a data acquisition module, a data fusion module, a state evaluation module, a trend prediction module, an early warning judgment module and an information interaction module. Key operation parameters are cooperatively acquired through multiple types of sensors, time series data are uniformly calibrated by adopting a timestamp mechanism, the problems of fragmentation of operation state information of power equipment and superposition of acquisition errors are effectively solved, and then feature fusion and dimension reduction compression are performed on high-dimensional heterogeneous data by introducing a principal component analysis and auto-encoder neural network, so that the operation state information of the power equipment is acquired. According to the method, redundant information is eliminated, meanwhile, key discrimination features are reserved, the sensing dimension of the system for the equipment operation state is more comprehensive, the representation capacity is higher, the Bayesian network and the support vector machine are adopted to jointly evaluate the equipment state health level, higher state recognition accuracy is achieved in a dynamic scene, and the method is suitable for popularization and application. And the model generalization ability is enhanced through historical samples, so that the equipment state can be judged more stably.

The invention provides an optical storage and charging cooperative control method and system based on multi-energy complementation. The method comprises the following sub-steps: respectively establishing a photovoltaic power generation model, an energy storage system model and a charging load model; acquiring historical illumination information, charging information and electricity price information, constructing a prediction model based on a neural network, and predicting and outputting illumination intensity, charging load demand power and electricity price in a future time period; constructing a target optimization function by taking the annual net cost and the power deviation rate as optimization targets; according to the method, the cooperative optimization control of the photovoltaic power generation, energy storage and charging system is realized, the annual net cost is reduced, the power deviation is reduced, the energy storage and charging cooperative control strategy is established, the target optimization function is solved by adopting the improved whale optimization algorithm, and the charging and discharging power sequence is generated according to the optimal solution obtained by solving and is input to the system for execution. And the operation efficiency and reliability of the whole system are improved.

The invention relates to the technical field of building appearance defect detection, in particular to a building appearance defect detection method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining the building information of a target building, and generating a hierarchical scanning path and a three-dimensional obstacle avoidance flight path, a visible light image, an infrared thermodynamic diagram and laser radar point cloud information are collected for space-time alignment processing, and an attention mechanism neural network is used for extracting multi-scale features to generate a detection report containing defect three-dimensional coordinates, damage levels and safety risk assessment. The method achieves the purpose of efficiently and accurately detecting the building appearance defects, can adapt to complex building structures and environmental conditions, supports defect trend prediction and maintenance decision, and remarkably improves the building safety management efficiency.

The invention discloses a multi-sensing and data physical fusion frozen soil hot water force response test system and method, and the system integrates an ultra-weak fiber grating, an active heating fiber, a miniature dielectric constant sensor and an optical frequency domain reflection technology, and constructs a freezing process-oriented temperature, moisture, ice content and strain synchronous monitoring network; the method comprises the steps of constructing a multi-field fusion data set based on thermal disturbance lag correction, temperature-strain decoupling and a moisture-strain residual term compensation mechanism, and extracting key criterion characteristics of an ice lens growth rate, frost heaving force evolution and a shear deformation change rate; and in combination with a physical information neural network (PINN) embedded into a hot water force control equation, risk identification and grade judgment of the freezing abnormal behavior are realized. The method supports indoor heating and water pressure linkage adjustment, realizes a complete closed loop of multi-source sensing-risk identification-response verification under model driving, and breaks through the bottlenecks of low multi-physics field coupling identification precision, large parameter cross interference and insufficient risk identification real-time performance of the existing monitoring technology.

Apparatuses, systems, and techniques are presented to predict gaze of an observer. In at least one embodiment, a network is trained to predict a gaze of one or more users based, at least in part, on one or more gazes corresponding to objects not always visible to the one or more users.

The invention provides a new energy photovoltaic dynamic inspection method and system based on artificial intelligence, and relates to the technical field of photovoltaic power station intelligent inspection. Inspection is triggered according to weather early warning, performance warning or timed tasks; initial path planning is carried out by combining terrain, weather and historical data, and the path is updated by dynamic obstacle avoidance through an RRT * algorithm; multi-modal data, including visible light images, infrared thermal imaging, EL detection data and positioning data, are acquired during inspection of the unmanned aerial vehicle; the unmanned aerial vehicle data and the ground sensor data are integrated to generate a unified fault feature matrix; positioning a defect area in real time by using a deep neural network, judging a defect type and dividing a fault level; and finally, the health degree of the photovoltaic system is scored according to the fault level, and the safe operation trend is analyzed. The multi-modal data real-time fusion and dynamic path planning are realized, the fault identification precision and the inspection efficiency are improved, the manual inspection cost and risk are reduced, and powerful support is provided for intelligent operation and maintenance of a photovoltaic system.

The invention provides a road slope data monitoring method under a complex geological condition, and relates to the technical field of road slope monitoring, and the method comprises the steps: carrying out the temperature compensation of a change rate, analyzing the thermal expansion characteristic of a material, calculating the deformation caused by the temperature change, correcting the monitored side length, and generating a temperature compensation side length; according to the temperature compensation side length and the surrounding rock rheological equation, correcting the two adjacent side lengths through the strain difference of the adjacent sides so as to obtain the final side length; combining the final side length with the reference interior angle to generate a topological structure; based on the topological structure, constructing a three-dimensional deformation index matrix containing a diagonal displacement difference, an adjacent side curvature ratio and an interior angle variation coefficient; and inputting the index matrix, the support structure strain space-time matrix and the rock mass fragmentation tensor into a space-time attention graph neural network together, and outputting a deformation evolution trend including a diagonal displacement difference, an adjacent side curvature ratio and an interior angle variation coefficient. According to the invention, high-precision real-time monitoring can be realized.

The invention relates to the technical field of image processing, in particular to an anti-collision beam weld defect detection method and system based on image processing, and the method comprises the steps: carrying out the image collection of a weld region of a produced anti-collision beam, and obtaining a gray image; the method comprises the following steps: performing initial partitioning on a grayscale image, respectively obtaining a local complexity index of each initial sub-block, obtaining at least two adaptive sub-blocks based on the local complexity index of each initial sub-block, and performing adaptive local histogram equalization on each adaptive sub-block to obtain a target grayscale image; the method comprises the steps of performing edge detection on a target grayscale image to obtain at least two edge pixel points, performing frequency domain conversion on the target grayscale image according to a gradient direction of each edge pixel point to obtain a frequency domain image, performing filtering processing and time domain conversion on the frequency domain image to obtain a denoised image, and identifying defects in the denoised image by using a neural network. And the defect detection efficiency is improved by inhibiting the periodic texture in the weld seam image.

An apparatus includes at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: determine information related to a usability of at least one image with an artificial intelligence model; and signal the information related to the usability of the at least one image with an artificial intelligence model.

The invention discloses a lithium ion battery health state evaluation method and device based on deconstruction physical information, a medium and a product, and relates to the technical field of lithium ion battery health state evaluation, and the method comprises the steps: extracting a multi-dimensional health factor, inputting the multi-dimensional health factor into a deep physical information neural network fusing a self-attention mechanism module and a Koopman neural operator module, and obtaining a deep physical information neural network; and outputting the SOH estimation value. By extracting the universal health factors suitable for multiple working conditions, the problem that the universality of the health factors is insufficient is solved; a self-attention mechanism is utilized to enhance features and reduce redundancy, and the perception ability of the model to key information is enhanced; physical information is deconstructed by means of a Koopman neural operator, a physical mechanism of battery degradation is fused into the model, and the physical interpretability of the model is enhanced; the deep physical information neural network is fused with multi-dimensional information for estimation, individual differences and complex working conditions of different batteries are effectively dealt with, and therefore high-precision and high-robustness lithium ion battery SOH estimation is achieved.

The invention relates to the technical field of coal mine safety monitoring, in particular to an underground coal mine dust concentration monitoring method and system based on multi-modal data fusion, and the method comprises the steps: synchronously collecting dust concentration time sequence data, dust image data, sound wave signal data and environmental parameters through a multi-modal sensor array deployed in an underground coal mine; carrying out preprocessing and feature extraction on the collected data; predicting the decomposed high-frequency and low-frequency component signals by adopting a long short-term memory neural network and a grey Markov model; when the environment humidity is greater than 80%, carrying out light scattering compensation on the predicted value; inputting various predicted values into an improved D-S evidence theory fusion device, and outputting a fusion dust concentration monitoring value; and when the threshold value is exceeded or the temperature and humidity composite condition is reached, an acousto-optic alarm is triggered and a spraying dust-settling device is started. According to the method, the problems of low precision of a single sensor, multi-source data conflict, high-humidity environment measurement deviation and insufficient time sequence and fusion precision in underground coal mine dust concentration monitoring can be solved.

The invention provides a plateau lake agricultural non-point source pollution treatment method which comprises the following steps: acquiring vegetation indexes and surface temperature field data by using a remote sensing satellite, and generating a pollution source space thermodynamic diagram in combination with water quality and soil parameters of ground sampling points; performing space-time alignment and data fusion on the thermodynamic diagram and real-time runoff and soil permeability data acquired by the hydrological sensor network, and constructing a structured pollution migration database; on the basis of the database, a hybrid neural network model embedded with physical constraints is utilized to predict pollutant concentration distribution within 72 hours in the future; inputting the predicted value into a multi-stage optimization controller, and generating a control parameter set comprising treatment intensity, engineering parameters and a fertilization ratio; generating a treatment strategy map covering the drainage basin through a GIS; and deploying an Internet of Things monitoring node to collect the treated water quality data to form a closed-loop control link. The treatment efficiency and effect can be improved, the treatment cost is reduced, and the negative influence on the ecological environment is reduced.

The invention discloses a product logistics management scheduling method based on a block chain, a medium and equipment, and the method comprises the steps: generating an initial transportation path scheme through collecting logistics transportation demand information and supply chain node data; constructing a decentralized logistics resource pool, and collecting and verifying transportation fluctuation parameters to generate a credible state matrix; inputting the initial transportation path scheme and the logistics resource credible state matrix into a graph neural network scheduling model, and outputting a dynamic scheduling scheme; compliance verification of task allocation and scheduling instructions is automatically executed through the smart contract; and finally, updating the scheduling execution result and the logistics resource credible state matrix to a distributed account book, and outputting a logistics management report. Through deep cooperation of the intelligent contract and dynamic path optimization, real-time credible scheduling of the transportation process is realized, the problems of insufficient dynamic response and multi-party cooperation trust deficiency of an existing logistics system are solved, and the reliability and efficiency of cold chain and other sensitive commodity transportation are improved.

The invention provides a double-mechanism cooperative type curved surface component water immersion ultrasonic intelligent detection method and system, and relates to the technical field of ultrasonic detection, and the method comprises the following steps: scanning a workpiece by using a double-mechanism water immersion ultrasonic detection system, collecting and processing an ultrasonic echo signal, and generating an ultrasonic C scanning image. And performing defect identification on the ultrasonic C scanning image by using the constructed multi-scale feature fused deep convolutional neural network model to obtain the type, position, size and confidence of the defect. Quantitative evaluation is carried out based on the defect recognition result, a report is generated, and meanwhile visual display and database management of the defect recognition result are achieved. Through double-mechanism cooperative scanning and deep learning intelligent identification, the precision and efficiency of curved surface component defect detection are improved, and automatic identification, quantitative evaluation and visual display of defects are realized.

The invention discloses an intelligent self-monitoring temperature management system for a box-type substation, and relates to the technical field of intelligent power grid equipment monitoring. The problems that an existing system is large in temperature measurement deviation, low in reliability, delayed in early warning, inaccurate in hot spot positioning and extensive in heat dissipation control are solved. According to the scheme, multi-source signals are acquired in parallel through a data acquisition module, and a temperature time sequence is extracted; a boundary calibration module is adopted to fuse data to generate a three-dimensional boundary condition; the multi-physical field solving module obtains an internal temperature / stress field; the physical information prediction module is fused with a heat transfer physical constraint training graph neural network to predict a hotspot migration trend; the hierarchical scheduling module is used for solving a fan and oil pump collaborative optimization instruction in real time based on model predictive control; according to the invention, the accuracy of internal temperature monitoring of the box transformer substation, the reliability of hot spot prediction and the accuracy of heat dissipation control are remarkably improved, the insulation life of equipment is effectively prolonged, and the operation safety and reliability of the system are improved.

The invention relates to the technical field of fault diagnosis, and discloses a photovoltaic power station intelligent analysis and fault intelligent diagnosis method and system. The method comprises the steps of collecting electrical and environmental data of a photovoltaic power station through a sensor network, transmitting the electrical and environmental data to a data processing unit for preprocessing and standardization, and extracting multi-dimensional features to construct a feature matrix; and inputting the feature matrix into a multi-scale pulse neural network for fault diagnosis to obtain fault type and position information. And evaluating the severity of the fault according to the fault result, generating a maintenance scheme, and finally sending the maintenance scheme to the terminal equipment through the management system. The intelligent fault diagnosis system based on the pulse neural network is introduced, and the fault propagation analysis, the maintenance priority distribution, the path planning and the resource matching optimization are combined, so that the problems of low fault diagnosis precision and low maintenance resource scheduling efficiency in the prior art are solved, and the operation and maintenance efficiency and the equipment reliability of the photovoltaic power station are improved.

The invention relates to the field of intelligent agriculture, and discloses an intelligent drip irrigation control method, system and device and a storage medium, and the method comprises the following steps: data collection: obtaining soil humidity data, environmental meteorological data and crop growth state data of a target area through a plurality of sensors, and transmitting the data to a data processing module; the system comprises a data acquisition module used for acquiring soil humidity data, environmental meteorological data and crop growth state data and transmitting the acquired data to a data processing and storage module; the device comprises a machine case shell, and an intelligent control chip, a wireless communication module, an electromagnetic valve and a water flow meter are installed in the machine case shell. By integrating a sensor network, intelligent analysis and a real-time feedback mechanism, the water demand of crops is accurately calculated, irrigation is automatically adjusted, and water resource utilization is optimized; a personalized irrigation plan is made by combining a deep neural network and time sequence analysis, and the water requirements of crops in different growth stages are ensured.

The invention discloses a hydration heat monitoring and dynamic regulation and control method and system in a mass concrete pouring process, and relates to the technical field of concrete pouring, and the method comprises the steps: pre-burying a plurality of dual-mode temperature measurement probes in concrete, collecting dual-mode temperature field data in the concrete in real time, and synchronously collecting environmental parameters; carrying out preprocessing, dynamic weight distribution and data fusion on the obtained dual-mode temperature field data; according to the unsteady state heat transfer equation and the hydration heat model, a finite element heat conduction model is constructed, and the total hydration heat and the temperature difference trend are inverted through actually measured temperature field data; predicting a concrete core and surface temperature difference and a surface and environment temperature difference in a future set time period by combining an LSTM neural network model; and according to the predicted temperature difference result, the cooling water flow rate and the cooling water temperature are subjected to closed-loop regulation. Through the combination of dual-mode sensor temperature measurement and heat inversion, the hydration heat temperature difference in the concrete can be predicted in advance, a cooling scheme is regulated and controlled in real time in a linkage mode, and temperature stress cracks are avoided.

A robotic surgical system includes a robotic manipulator configured to perform surgical procedures under direct surgeon control. A surgical camera system captures real-time intraoperative video. An external imaging interface receives multimodal imaging data, including preoperative and intraoperative data from at least one of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and fluoroscopy. An artificial intelligence (AI module has a trained neural network and a deep learning model trained on multi-institutional annotated surgical datasets, The AI module is configured to execute one or more of: fuse acquired video and imaging data into temporally and spatially coherent anatomical visualizations; generate continuously updating overlays aligned with the surgical field, with segmented anatomical features; projected tissue boundaries, proximity indicators for instruments, and predictive deformation trends; provide dynamic predictive trend visualization indicating zones of future anatomical complexity or risk; register and align preoperative imaging data with intraoperative imaging data in real time; adapt overlay presentation in response to tissue deformation without actuating the robotic manipulate or; and passively augment visual feedback without initiating any autonomous actuation of surgical instruments.

The invention relates to the technical field of image recognition, and discloses a flame-retardant material surface defect image recognition method, device and equipment and a storage medium, and the method comprises the steps: carrying out the multi-angle image collection and preprocessing of a flame-retardant material combustion test sample, and obtaining a standardized multi-view image data set; performing multi-model feature extraction and feature matching processing to obtain a material defect representation vector set and a defect semantic feature set; constructing a self-adaptive connection structure and a hierarchical defect map; performing multi-layer information transmission and topological relation explicit modeling through a depth map neural network to obtain a defect node depth representation set and a material defect relation matrix; domain invariant feature extraction and structural consistency constraint are carried out, material-independent defect type distribution and defect severity quantification results are obtained, defect features of all angles of the surface of the flame-retardant material can be comprehensively captured, remote interaction characteristics between defects are extracted, and the discrimination capability of defect feature representation is enhanced.

The invention discloses a radio frequency signal interference intelligent suppression management system and method based on image recognition. The system comprises a multi-source sensing module, a fusion analysis module, a dynamic suppression module and an execution feedback module. The multi-source sensing module forms a radio frequency original signal set containing frequency spectrum features and time domain waveforms; the fusion analysis module receives the radio frequency original signal set and extracts an interference source feature vector in the radio frequency original signal set through a pre-trained convolutional neural network to generate a fusion decision signal; the dynamic suppression module receives the fusion decision signal and activates a corresponding algorithm in a preset suppression strategy library according to the interference type identifier; and the execution feedback module receives the real-time control signal and drives the tunable filter, the phased-array antenna and the power amplifier to execute interference suppression operation. According to the intelligent suppression management system and method for the radio-frequency signal interference based on image recognition, the problems of fuzzy positioning of the radio-frequency interference source, single suppression strategy and lack of environmental perception capability can be solved.

The invention relates to a lithium battery electrochemical impedance spectroscopy online measurement and estimation method and system based on a charging pile, and belongs to a battery detection technology. The system comprises a measurement module, an estimation module and a result output module. The measurement module comprises a signal generation sub-module, a waveform amplification sub-module and a response processing sub-module; a pulse control signal is inserted into the signal generation sub-module during charging, and is amplified into a measurement excitation signal by the waveform amplification sub-module to be applied to a battery; the response processing sub-module samples the response signal and calculates an impedance spectrum. The estimation module comprises a neural network sub-module and an impedance spectrum estimation sub-module; the neural network sub-module is used for training a physical information neural network by using impedance response to output Randles circuit model parameters to the estimation sub-module, and the change of the electrochemical impedance spectrum is predicted. And the result output module displays and stores the measured and estimated values of the impedance spectrum. According to the invention, the impedance values of the to-be-measured battery pack under multiple frequencies can be measured and estimated on line, and the estimation of the impedance spectrum is physically interpretable.

The invention discloses a cable life dynamic evaluation system based on multi-physics field coupling, and particularly relates to the field of industrial automation and control systems, which comprises a multi-physics field sensing module, a coupling analysis engine module, a dynamic life evaluation module, a digital twin interaction module and an environmental interference suppression module, through a distributed optical fiber temperature sensor, a capacitive electric field sensor and a magnetostrictive stress sensor, temperature, electric field, magnetic field and mechanical stress data of a cable are collected in real time, multi-physical field characteristics and a cable defect database are matched in real time by using a cross-scale dynamic association algorithm, a damage state is evaluated, and the cable defect detection accuracy is improved. A time sequence neural network architecture is adopted to predict the remaining life, model self-correction is achieved through digital twin comparison, interference is suppressed in combination with an environment-physical field coupling compensation matrix, sensing and evaluation of the health state of the cable, life prediction and continuous optimization of the model are achieved, and the efficiency of cable life evaluation is improved.

The invention relates to the technical field of online car-hailing order dispatching, in particular to an online car-hailing intelligent order dispatching method and system based on a multi-dimensional rule, and the method comprises the steps: building a standardized multi-dimensional feature set through collecting passenger orders, driver states, traffic conditions, environmental weather and regional events, and carrying out the combined modeling of regional order demands and driver online conditions based on a recurrent neural network, thereby achieving the intelligent order dispatching of the online car-hailing. Outputting expected passenger and driver thermodynamic distribution, constructing expected difference thermodynamic distribution according to the expected passenger and driver thermodynamic distribution, training a scheduling strategy through reinforcement learning, enabling an unloaded vehicle to actively migrate to a supply and demand gap area before an order is generated, and further combining an order generation condition and driver expected off-duty information to obtain an order generation result; and calculating the accumulated driving time required by the driver to return to the expected off-duty place after the driver completes the service, and selecting the work order for distribution when the time difference between the accumulated driving time and the expected off-duty place is minimum, thereby realizing accurate matching between the order distribution strategy and the driver work cycle. The empty driving rate is effectively reduced, and the quick response capability and the resource configuration efficiency of the system in a dynamic supply and demand environment are improved.