682 results about "Cloud modeling" patented technology

The invention relates to the technical field of building energy saving, in particular to a building energy-saving management system and method based on the Internet of Things. The method comprises the following steps: acquiring a target architectural design drawing; building structure feature point recognition is carried out on the target building design drawing, three-dimensional space point cloud modeling is carried out, and a building three-dimensional structure model is constructed; building environment illumination parameters and personnel flow monitoring parameters are collected based on multiple sensor nodes; environment light intensity space distribution analysis is carried out on the building environment illumination parameters, then dynamic light intensity distribution rendering is carried out, and an environment light intensity distribution rendering model is constructed; and according to the building three-dimensional structure model, carrying out regional use scene analysis on the environment light intensity distribution rendering model, and carrying out optimal time sequence illumination demand fitting so as to construct an optimal illumination demand time sequence curve of each region. The energy consumption utilization rate of the building is increased, and accurate equipment regulation and control are achieved.

The invention relates to the technical field of rail transit, and discloses an intelligent diagnosis method and system for the health degree of urban rail equipment based on a cloud platform and a medium, the intelligent diagnosis method for the health degree of the urban rail equipment based on the cloud platform comprises the following steps: S1, multi-source data collection and edge preprocessing: collecting operation data of the urban rail equipment through a sensor, performing time sequence alignment and abnormal value filtering on the data to generate preprocessed data; s2, triggering type data uploading: based on the preprocessed data, judging whether an uploading condition is met or not through a statistical rule or a model prediction result, and uploading the data meeting the condition to a cloud platform; and S3, cloud model training and health degree prediction: constructing an equipment health state prediction model through a transfer learning algorithm by using the uploaded data. According to the invention, through deep learning modeling, edge calculation deployment and a multi-factor early warning mechanism, efficient evaluation, stable early warning and low-delay response of the equipment state are realized.

The invention provides a cue word generation method and device, equipment, a medium and a program product, and relates to the technical field of computers, in particular to the technical field of artificial intelligence, natural language processing and deep learning. According to the specific implementation scheme, user input content is obtained; based on constraints of a user knowledge base and / or a user intention rule base, adopting an intention recognition model to perform cue word intention recognition on user input content, and outputting cue words reflecting user intentions; and inputting the cue word into the cloud model to obtain an output result. According to the embodiment of the invention, the generation accuracy of the cue word can be improved.

The invention discloses an intelligent traffic data analysis and decision generation system based on multi-model dynamic collaboration, and the system comprises a multi-mode collection module which collects voice, text, video and structured data and loads a traffic knowledge graph; the feature processing module is used for extracting semantic, time sequence and spatial features and generating a fusion vector; the semantic modeling module is used for generating an intention vector in combination with the knowledge graph and the interaction record; the dynamic routing module selects an edge or cloud model according to the intention vector to generate a query statement; the Prompt memory module fuses the historical template and the alignment parameters to generate a query draft; the query verification module is used for executing semantic and structure verification and outputting a correction statement; the query execution module generates a response vector; the causal interpretation module generates an interpretation vector; and the response generation module outputs a multi-granularity result according to the user role. According to the method, the cooperative processing capability and semantic analysis precision of complex traffic query are improved.

The invention relates to the technical field of photovoltaic power generation, and discloses a BAPV-based roof photovoltaic intelligent monitoring system and a dynamic tuning method and device. The method comprises the following steps: scanning a preset BAPV assembly through three-dimensional laser, constructing a surface point cloud model, and establishing a three-dimensional coordinate system comprising BAPV assembly installation base points; acquiring a real-time environment parameter set corresponding to the BAPV component; calculating an optimal working angle of the BAPV component according to the real-time environment parameter set based on an improved particle swarm algorithm; simulating an all-weather shadow movement track according to the optimal working angle based on a Monte Carlo method, and generating a component shielding influence weight matrix; establishing a heat balance equation according to the component shielding influence weight matrix so as to calculate a spacing optimization parameter of the BAPV component; a control instruction is generated according to the spacing optimization parameters, and dynamic tuning of the BAPV assembly is completed according to the control instruction; the control instruction comprises a support rotation angle compensation amount, an inverter parameter adjustment amount and an abnormal component isolation instruction. And the pain points of modeling errors, static optimization limitation, thermal runaway risks and the like are solved.

The invention relates to the technical field of model construction, in particular to a method and a system for establishing a parameterized tunnel model based on cloud computing. The method comprises the following steps: acquiring a tunnel section type, and extracting structural semantics to obtain a section feature description set; constructing a parameter dictionary of the tunnel section based on the section feature description set to obtain a dynamic parameter dictionary data set; and modeling a tunnel section geometric structure based on the dynamic parameter dictionary data set to obtain an initial three-dimensional tunnel geometric model. Through dynamic parameter management, a self-correction mechanism and a Latin hypercube sampling technology, efficient automatic modeling and precise simulation optimization of the tunnel section three-dimensional geometric model are achieved, meanwhile, through unified packaging and authority control, safe and efficient management and collaborative sharing of cloud model resources are achieved, and the method is suitable for large-scale popularization and application. And the intelligent level and the engineering efficiency of tunnel model construction and application are comprehensively improved.

The invention relates to a probe contact impedance real-time monitoring and compensating method and device and a storage medium. The method comprises the following steps: acquiring a first measurement data set containing a probe contact resistance value and a contact force, and inputting the first measurement data set into a double-layer deep learning neural network for training to obtain a probe contact resistance monitoring model; performing two-parameter fault detection to obtain a contact state classification result; generating a two-dimensional compensation matrix based on a contact state classification result, and performing nonlinear mapping calculation on the second measurement data set to obtain a compensation coefficient; the compensation coefficient, the original probe structure parameters and the original probe operation parameters are input into a graph attention stacking auto-encoder for parameter prediction, and target probe structure parameters and target probe operation parameters are obtained; cloud model calculation and weighting operation are carried out, a target compensation instruction is obtained, and the target compensation instruction is used for adjusting the probe position parameters and the probe pressure parameters. According to the invention, the probe testing precision of the chip is improved.

The invention relates to the technical field of BIM, in particular to a BIM-based construction progress intelligent management and control method and system, and the method comprises the steps: constructing a BIM digital twin cloud model, fusing a panoramic image and LiDAR point cloud data, dynamically selecting a data source update model through an algorithm, extracting engineering topology, building a construction network diagram, and generating a state evolution trajectory in combination with environment and resource data. Predicting and visualizing milestone time, comparing field data with a BIM model to generate progress deviation information, performing stability analysis, triggering resource allocation when a threshold value is exceeded, synchronizing a material supplier and a field manager, dynamically adjusting personnel, equipment and materials, dynamically adjusting a milestone plan based on multiple data, and presenting and guiding allocation through the BIM model. The construction progress is ensured to be consistent with the plan, and the complex interaction relationship and dynamic characteristics in the construction process are captured through nonlinear dynamic system modeling in combination with LiDAR point cloud and other high-precision data.

The invention discloses a large language model low-voltage power distribution network topology perception method based on cloud edge fusion, and relates to the technical field of power systems, and the method comprises the steps: collecting the data of a low-voltage power distribution network through an edge device, and analyzing a topological structure through a cloud model; standardizing the power distribution network data based on a large language model, and verifying topological information; and establishing an n-shaped model to realize direct-current and alternating-current power distribution network differential modeling, and based on the standardized power distribution network data, automatically generating a standard power distribution network model and generating power distribution network topology output. According to the method, low-voltage power distribution network data is collected through edge equipment, a topological structure is analyzed through a cloud model, multi-data source input is supported, topological information is verified, standardized processing and data quality control are achieved, direct-current and alternating-current power distribution network differential modeling is achieved by establishing an n-shaped model, and the modeling efficiency is improved. The calculation efficiency and precision of the model are improved, the application efficiency of topological data is improved through automatic output, and the multi-scene requirement is met.

The invention relates to the technical field of aluminum formwork stacking planning, in particular to a stacking planning control system and method for building aluminum formworks. The method comprises the following steps: acquiring a multi-angle monitoring image of a building aluminum template to be stacked, performing structural feature point visual identification on the multi-angle monitoring image of the building aluminum template, performing three-dimensional form point cloud modeling, and constructing a three-dimensional structure model of each aluminum template; obtaining a building aluminum template production operation log; physical state feature analysis is carried out based on the building aluminum formwork production operation log, stacking planning constraint rule evolution is carried out, and therefore an aluminum formwork overall stacking planning constraint rule is obtained; and intelligent stacking planning is carried out on each aluminum template three-dimensional structure model based on an aluminum template overall stacking planning constraint rule, optimal stacking planning evaluation is carried out, and an optimal aluminum template stacking planning scheme is extracted. According to the invention, stacking planning control with high space utilization rate and high efficiency is realized.

The invention discloses an industrial inspection intelligent decision-making method and system based on large and small model collaboration. The industrial inspection intelligent decision-making method comprises the following steps: dividing an inspection task into a plurality of sub-tasks by using a cloud large model, and performing dynamic task planning based on the sub-tasks; calling an edge small model to execute the subtask to obtain multi-modal original data; performing cross-modal feature fusion on the multi-modal original data by using a cloud large model to obtain cross-modal fusion features; and reasoning based on the cross-modal fusion features by using a cloud large model, extracting abnormal factors, and formulating a new inspection task based on analysis of the abnormal factors. Through the comprehensive method of comprehensive multi-mode perception, conditional diffusion model enhancement and cloud-edge collaborative decision, the problems of fault sample scarcity and perception deviation in an extreme environment are effectively solved, high precision and low time delay are considered, and a new breakthrough of intelligent inspection is brought.

The invention relates to an edge cloud collaborative learning method and system based on cloud model decomposition. The method belongs to the technical field of machine learning and data processing, and comprises the following steps: a cloud server performs model training to obtain a centralized cloud model, and the centralized cloud model is decomposed into a plurality of sub-models; each edge device determines a target sub-model and adjusts the target sub-model to obtain a customized sub-model; all the edge devices send model updating parameters in the model training process to the cloud server, and the cloud server updates a global model according to the model updating parameters; migrating knowledge of the global model back to the edge device through a knowledge distillation technology; and the edge device performs fine adjustment on the global model according to the local data set to realize edge-cloud collaboration. Through effective knowledge exchange between the edge device and the cloud, continuous learning and global optimization of the model are realized, and the method is more flexible, expandable and high in adaptability.

The invention discloses a life prediction method, device and system for a chain transmission system oriented to multi-component coupling, and the method comprises the steps: carrying out the topological structure decomposition of multiple components of the chain transmission system, and extracting the fault relation between the components of the system through combining historical fault data and expert knowledge; preprocessing the fault data to obtain a structured fault feature matrix; establishing a fault propagation network of the chain transmission system by using the cloud model symbol directed graph, and constructing a modularized fault propagation path reasoning model based on the functional relationship and working condition change of system components; dynamic simulation and real-time analysis are carried out on fault propagation of the system through compatible pathway search and qualitative and quantitative conversion of a cloud model; the random uncertainty and the cognitive uncertainty are quantified in a unified mode, and a regression model is adopted to conduct modeling on the degradation trend of the chain transmission system; and performing model parameter optimization, performing residual life prediction of the chain transmission system in combination with a first arrival time method, and giving out the confidence coefficient of life prediction through confidence interval analysis.

The embodiment of the invention provides a geological disaster real-time early warning method based on multi-source data dynamic fusion. Comprising the steps that physical parameters of various monitoring data capable of being acquired in a monitoring area are determined and preprocessed; dividing early warning levels, and determining interval values of different early warning levels corresponding to each type of monitoring data; calculating a cloud model parameter value of each early warning level of each type of monitoring data; calculating the credibility of each early warning level corresponding to each type of monitoring data based on the parameter values; based on the credibility distribution, calculating the consistent quantity, the conflict quantity, the conflict intensity and the unreliability among the multi-source monitoring data; constructing an early warning level credibility redistribution equation, and obtaining new credibility of each early warning level corresponding to each piece of monitoring data; and fusing the new credibility of the early warning level, obtaining the credibility of the early warning level based on multi-source data fusion, and determining early warning information and dynamically updating an early warning result in combination with a maximum credibility principle. According to the method, the problem of multi-source data conflict fusion is solved, and the geological disaster early warning efficiency is improved.

The invention relates to the technical field of intelligent control of the welding process, and discloses an online monitoring system and control method for the friction stir welding process, and the system comprises a multi-mode sensor array module which is used for collecting temperature, pressure and displacement data in the welding process; the edge computing node module preprocesses the collected data and extracts spatial-temporal characteristics; the space-time diagram convolutional network module is used for mining a space-time association relationship between the sensors; the hierarchical reinforcement learning controller generates a control instruction according to the associated features; the execution mechanism module receives the control instruction and outputs a welding signal; the digital twinborn verification module performs simulation feedback on the control effect; and the cloud model optimization module fuses the multi-source feedback information to continuously optimize the control strategy. The welding process self-adaptive control technology based on reinforcement learning is adopted, the technical effect of adjusting the welding parameters in real time to optimize the welding quality is achieved, and the problem that in a traditional method, the welding quality is unstable due to working condition changes is solved.

The invention relates to the technical field of construction progress management, in particular to a building construction progress intelligent tracking and pushing method and system based on BIM, and the method comprises the following steps: S1, constructing a 3D-BIM model, building a mapping relation, and forming a progress plan model; s2, collecting dynamic construction progress data of a construction site, and generating a construction real spot cloud model; s3, based on the generated construction real spot cloud model, analyzing the construction progress monitoring data difference between the Ti time period and the Ti + 1 time period, and evaluating the actual construction speed; s4, comparing the construction progress deviation between the actual construction speed and the planned construction speed, and judging whether progress delay exists or not; and S5, when progress delay exists, identifying and classifying delay reasons, generating a subentry progress deviation report, and pushing a deviation correction scheme. According to the invention, dynamic and real-time visual progress tracking and control are realized, the real-time requirement of project design file progress dynamic tracking is met, and the fine management level of construction progress tracking is improved.

The invention discloses a dynamic data closed-loop system based on real-time positioning confidence evaluation and an optimization method. The dynamic data closed-loop system and the optimization method are suitable for high-precision positioning scenes such as automatic driving and unmanned systems. The system collects data of a GPS, an IMU, a laser radar and a camera through a multi-source sensor, and a confidence evaluation module is adopted to fuse sensor data quality, environment interference indexes and historical track consistency to calculate positioning confidence. Based on confidence score, the system triggers different data acquisition strategies and sensor parameter configurations, and dynamically optimizes resource allocation. The system comprises an edge end fusion optimization module and a cloud model iteration module, constructs a closed loop mechanism, uploads data to a cloud for model updating and returns parameters when the confidence coefficient is abnormal, and improves the adaptability and robustness of the system. Experiments show that the method can effectively reduce the positioning error, optimizes the use of computing resources, and has good real-time performance, expansibility and engineering feasibility.

The invention provides a large model ecological online evolution learning method based on end-cloud collaboration and an application thereof, and the method is applied to the field of e-commerce, so that efficient training and deployment of an e-commerce model are realized. According to the method, an end-cloud collaborative architecture is adopted, a cloud end deploys a large language model, an end side uses a small and medium-sized language model, and efficient training and reasoning of large model ecology are realized through collaborative work of the cloud end and the end side. In the training process, the cloud model serves as a teacher model, a pseudo tag is generated to supplement a data set, and optimization training of the end side model is guided. Meanwhile, a multi-model cooperation self-training mechanism is introduced, multiple models play different roles and mutually discuss, training labels are autonomously generated, and the end side model is further optimized. And meanwhile, user feedback information is brought into cue words of the cloud model for situational learning, so that the model performance is continuously optimized. The method is applied to an e-commerce scene, the e-commerce model ecology is successfully constructed and deployed, and intelligent development of the field is realized.

According to the subway construction safety system toughness evaluation method based on the cloud model provided by the invention, the dynamic process attributes of toughness realization and the functions of the components of the subway construction safety system in the toughness realization process are considered; the absorbing force subsystem, the restoring force subsystem and the adaptability subsystem based on the toughness process are combined with the personnel subsystem, the material subsystem, the technology subsystem, the management subsystem and the environment subsystem of the subway construction safety system, and a scientific and comprehensive subway construction safety system toughness evaluation index system is constructed. Secondly, considering different importance of different indexes, considering fuzziness and randomness of evaluation grade and dimension division and index selection in a subway construction safety system toughness evaluation process, and adopting a CRITIC method to calculate index weight and a cloud model to construct a subway construction safety system toughness evaluation model; and finally, in order to further clarify the toughness grade, calculating the similarity with the standard cloud through the fuzzy closeness, and determining the toughness grade according to the grade characteristic value.

The invention relates to the field of data management, in particular to a VOCs pollution working condition data treatment method based on artificial intelligence. The method comprises the following steps: firstly, constructing a three-dimensional model of a factory, a pipeline and treatment equipment based on a BIM tool and a GIS technology, and deploying an Internet of Things sensor to collect VOCs pollution data; collecting original infrared absorption spectrum data of VOCs, and performing component identification by using a non-negative matrix factorization algorithm; a machine learning model is trained in combination with historical data, and short-term emission prediction is carried out; cooperative scheduling is carried out on the governance equipment through a multi-agent game optimization algorithm, and an optimal operation scheme is generated; constructing volume cloud modeling, and dynamically displaying an optimization effect; and if the predicted emission exceeds the standard, performing emission abnormity traceability analysis by using a graph neural network, and outputting a fault diagnosis report. According to the method, the VOCs emission prediction precision and the treatment efficiency are effectively improved.

The embodiment of the invention provides a data analysis method, electronic equipment, a wearable equipment system and a storage medium, is applied to the electronic equipment, and belongs to the technical field of artificial intelligence. The method comprises the steps of obtaining original environment data of a target object in response to question information of the target object; performing complexity analysis according to the problem information and the original environment data to obtain complexity level information; the network signal intensity of the target network is acquired, and the electronic equipment is networked with the cloud server through the target network. And selecting a target model from candidate models according to the complex level information and the network signal intensity, wherein the candidate models comprise at least one of a local model deployed in the electronic equipment and a cloud model deployed in a cloud server. And then performing question answering on the original environment data and the question information according to the target model to obtain target answer data, and pushing the target answer data to the target object. The embodiment of the invention can improve the response speed of the electronic equipment.

The invention relates to the technical field of intelligent assessment, and discloses a dam risk assessment method, device and equipment based on multi-source data fusion, and the method comprises the steps: determining a monitoring project type of dam multi-source monitoring data, building a risk assessment index system through selecting a key index of the monitoring project type, and obtaining a risk assessment result; the method comprises the steps of dividing a dam region by combining measuring point spatial distribution to obtain a dam partitioning result, processing single measuring point actual measurement data of the same region in the dam partitioning result through a dam risk assessment model to obtain a region basic probability distribution value, and further obtaining a risk assessment result. According to the method, the limitation of single data dimension is broken through by constructing a risk assessment index system, accurate mastering of risk heterogeneity of different regions is realized through region division, a dam risk assessment model combining a cloud model and an evidence theory is adopted, the problem of dimension unification of multi-source data is solved, the risk assessment comprehensiveness is ensured, and the risk assessment efficiency is improved. And the accuracy of risk assessment is also improved.

The invention discloses a flexible automatic production management and control system for a new energy high-power high-frequency transformer, and relates to the field of production management and control, the flexible automatic production management and control system comprises a production demand receiving module, a production resource management module, a production planning module, a production execution module and a quality control module, an order is received through an API or EDI, and a work order is generated; collecting equipment and material information in real time, and constructing a production resource association network; carrying out production scheduling and process matching by adopting an improved deep learning model; scheduling materials, procedures and equipment based on a multi-level analysis control model; the quality is evaluated by using a two-dimensional cloud model, and data recording is performed through a distributed database, so that the traceability of the quality problem is ensured. According to the system, the production efficiency and the quality management level are effectively improved, the problem that existing rigid automatic equipment lacks flexibility is solved, and efficient and accurate production of the new energy high-power high-frequency transformer is ensured through cooperative work of a plurality of modules so as to meet different production requirements.

The invention relates to the technical field of rail traffic equipment maintenance and artificial intelligence image recognition, in particular to a rail vehicle cloud edge cooperative detection method and system and inspection equipment. The method comprises the following steps: acquiring image data of an area to be detected, synchronously recording attitude information of shooting equipment, carrying out image quality scoring on the image data, if the image quality score reaches a preset threshold value, calling a local target detection model to carry out defect identification on an image, and outputting an identification result containing a target type, a position and confidence; if the confidence coefficient of the recognition result is lower than a set threshold value, the image data and the posture information are uploaded to a cloud server, a cloud high-precision recognition model is called for secondary recognition, and a cloud recognition result is obtained. The real-time requirement is met through quick response of the local model, the cloud model rechecks a low-confidence result, efficiency and precision are both considered, and the contradiction that in the prior art, manual judgment standards are different, and speed and precision are difficult to consider is solved.

The embodiment of the invention relates to a cloud road collaborative learning method for a traffic multi-mode large language model, and the method comprises the steps: constructing and training a traffic multi-mode large language model, and recording the traffic multi-mode large language model as a cloud re-model at a cloud; compressing the model and deploying the compressed model at the roadside and the cloud at the same time to be marked as a roadside / cloud light model; the roadside inputs the real-time data into a roadside light model for visual target detection and traffic condition identification processing, and sends model input / output data to the cloud; the cloud inputs the input data of the roadside model into the cloud re-model for visual target detection and traffic condition recognition processing, and the input / output data of the roadside / cloud model form a roadside-cloud record and stores the roadside-cloud record; the cloud periodically updates the cloud light model according to a knowledge distillation method based on a data set formed by roadside-cloud records and sends a parameter difference before and after updating to the roadside; and the roadside updates the roadside light model based on the parameter difference sent by the cloud. Based on the invention, the performance of the roadside model can be improved.

The invention provides an equipment abnormal voiceprint detection system. The equipment abnormal voiceprint detection system comprises a voiceprint collection module used for collecting original voiceprint signals in real time and preprocessing the original voiceprint signals; the edge end detection module is deployed on edge computing equipment and is used for carrying out real-time anomaly detection on the voiceprint features by utilizing a one-dimensional lightweight neural network model; the abnormity credibility evaluation module is used for converting a real-time abnormity detection result into a probabilistic abnormity credibility score; the incremental data screening module is used for screening high-value samples from the real-time voiceprint data based on a dynamic density trend sensing algorithm and caching the high-value samples in the edge; the cloud model evolution module is used for carrying out evolution training on the detection model by utilizing a continuous learning mechanism; the generation and playback module is used for jointly generating pseudo samples through a variational auto-encoder VAE and a generative adversarial network GAN; and the model updating module is used for compressing the evolved cloud model and then issuing and replacing the original model in the edge end detection module so as to form a cloud-edge collaborative sustainable evolution closed loop.

The invention relates to a wind turbine generator set anomaly detection method considering multi-unit state similarity. Firstly, an evaluation index system is constructed, and design parameters and historical operation data are analyzed. Then data acquisition and preprocessing are carried out, an SCADA system is used for acquiring data, and denoising is carried out in combination with a local outlier factor algorithm and wavelet transform; and then qualitative evaluation is carried out, a cloud model is constructed to describe data uncertainty, a membership degree is calculated, and a cluster is dynamically adjusted. And then comprehensive evaluation is carried out, index weights are determined through an analytic hierarchy process, and scores are calculated in combination with membership degrees. And finally, performing anomaly detection, setting a threshold value, combining a dynamic clustering method, and taking a quantile of a sliding time window and a similar unit prediction residual error as a real-time early warning threshold value. According to the method, the accuracy and reliability of wind turbine generator anomaly detection are improved.

The invention provides a three-dimensional scene simulation method and system based on 3D Gaussian sputtering in the technical field of physical simulation. The method comprises the steps that S1, an initial point cloud model is obtained based on reconstruction of all object images; s2, based on the initial point cloud model, mask label reconstruction is carried out to obtain a triangular mesh model, and a tetrahedral mesh model is obtained through expansion; s3, creating a 3D Gaussian sputtering model based on the triangular mesh model through a 3D Gaussian kernel function; s4, through implicit integration and finite element modes, controlling the tetrahedral mesh model to simulate the deformation and motion effect of the simulation target based on physical attributes, and collecting the deformation gradient and vertex coordinates of each tetrahedral mesh in the deformation process; s5, synchronously deforming the 3D Gaussian sputtering model based on the deformation gradient; and S6, the deformed 3D Gaussian sputtering model is mapped to the tetrahedral mesh model based on the vertex coordinates, and a simulation result is obtained through rendering. The method has the advantage that the three-dimensional scene simulation precision is greatly improved.

The invention relates to the technical field of intelligent monitoring of petroleum equipment, and particularly provides an industrial equipment state diagnosis method based on multi-modal fusion deep learning. The method comprises the following steps: synchronously acquiring load, vibration and current data through an explosion-proof RTU (Remote Terminal Unit), and transmitting the data to an edge node through an industrial LoRaWAN (Load RaWide Area Network); aligning a multi-source signal time sequence by adopting Kalman filtering, and fusing to generate data streams with consistent time domains; features are extracted by using wavelet packet energy entropy, and a lightweight ResNet18 model is input to realize high-precision diagnosis; based on the diagnosis result, a dynamic optimization strategy is generated through an LSTM-Transform parallel model; and finally, issuing a control instruction to the equipment for execution, and collecting feedback data to update the cloud model. Through the multi-rate signal time sequence alignment and feature-decision two-stage fusion technology, the problem of diagnosis errors caused by data asynchronization in a traditional method is solved, the fault recognition precision and the equipment energy efficiency are remarkably improved, and meanwhile the requirements for explosion prevention and real-time performance of the industrial environment are met.

The invention relates to the field of smart agriculture, in particular to a smart agriculture Internet of Things monitoring, fertilizer preparation and irrigation integrated automatic control method and system, and the method comprises the steps: monitoring soil detection data in real time; whether the soil detection data meet irrigation conditions or not is judged by combining the soil detection data and planting variety data corresponding to the soil position; when the soil detection data meets the irrigation condition, sending the soil detection data and the planting variety data to a preset cloud model based on a judgment result, calculating fertilizer supplement data through the cloud model, and outputting a calculation result; generating irrigation parameters of the target irrigation position according to the fertilizer supplement data; the irrigation parameters are sent to an irrigation end, fertilizer is blended through the irrigation end, and the target irrigation position is irrigated. Through an intelligent control system integrating networking monitoring, fertilizer preparation and irrigation, the use of water and fertilizer is optimized, and the hysteresis of data is greatly reduced. Irrigation and fertilization strategies can be quickly adjusted.