10998 results about "Data input" patented technology

The invention provides a micro-grid cooperative scheduling method and device, and relates to the technical field of smart grids, and the method comprises the steps: obtaining historical operation data and real-time operation data of a micro-grid system, and data of an external information system; generating load demand and energy equipment output prediction information based on the historical operation data and the data of the external information system; constructing a layered multi-time-scale decision architecture, and performing decision optimization on each layer of agents by adopting a reinforcement learning algorithm; constructing a plurality of heterogeneous agents, and carrying out cooperative scheduling on the plurality of heterogeneous agents by adopting a centralized training and distributed execution multi-agent reinforcement learning algorithm; inputting the prediction information and the real-time operation data into a decision framework, and outputting a real-time control instruction; and setting a security constraint condition, and realizing optimization of the security constraint in combination with a Lyapunov function, a Lagrange multiplier method, a security layer mechanism and a reinforcement learning algorithm. According to the method provided by the invention, the safe, efficient and reliable operation of the micro-grid in the grid-connected / off-grid mode can be realized.

The invention provides a fatigue life prediction and residual life evaluation method for a high-power heavy-duty gearbox, and belongs to the technical field of intelligent operation and maintenance based on computer data processing. Comprising the following steps: acquiring dynamic data in an operation process, and performing multi-scale decomposition to form multi-source multi-scale data; inputting the multi-source multi-scale data into a designed multi-scale fatigue feature extraction module and a health state prediction module to obtain a multi-scale health index sequence and a health state label; establishing a fatigue damage evolution model, introducing the generated health index sequence for self-adaptive updating, outputting a comprehensive damage value, performing staged evaluation of fatigue degradation to obtain a damage label set, and performing multi-scale health index sequence and health state labels as well as the comprehensive damage value and the damage label set to obtain a multi-scale health index sequence and health state labels; inputting into a designed double-source fusion fatigue life prediction model, and outputting residual life prediction quantity; according to the invention, high-precision prediction and residual life evaluation of the fatigue life of the high-power heavy-duty gearbox are realized.

The invention relates to an underwater robot navigation positioning method and system. The method comprises the following steps: S1, acquiring angular velocity and acceleration signals through an inertial measurement unit; s2, resolving a three-dimensional velocity observation value according to the beam radial velocity vector signal in combination with the angular velocity signal, and extracting environment feature point cloud data according to the acoustic image signal; s3, multi-source data time synchronization is carried out, and a fusion input signal with time-space alignment is generated; s4, constructing an adaptive factor graph optimization model, and dynamically adjusting an inertial navigation solution node based on a real-time weight coefficient; inputting the environment feature point cloud data into a closed-loop detection module to generate a loopback factor node, and adaptively correcting the weight of the node according to the feature matching degree; and S5, solving the adaptive factor graph optimization model through a nonlinear optimization algorithm. According to the underwater robot navigation positioning method and system, the problem that the fusion positioning precision of a multi-source heterogeneous sensor is insufficient in an underwater GPS-free environment can be solved.

The invention relates to an electric hand drill wear state prediction and health management system, which belongs to the technical field of intelligent fault diagnosis and predictive maintenance of industrial equipment, and comprises a data acquisition and preprocessing unit used for acquiring and processing a multi-modal physical signal to generate a standardized data frame; the multi-domain feature transformation unit is used for receiving the standardized data frame and transforming the standardized data frame into a health feature vector and a load feature vector; the dynamic health baseline construction unit is used for reconstructing and generating a dynamic health baseline through a depth generation model according to the time sequence of the health feature vector and the load feature vector; and the residual error sequence generation and statistical monitoring unit is used for calculating the distance between the health feature vector and the dynamic health baseline, generating a residual error sequence, and performing statistical processing on the residual error sequence to obtain a statistical magnitude. According to the invention, the interference of working condition change on health state assessment is eliminated, and pure and reliable data input is provided for subsequent accurate monitoring.

An interface circuit includes a reference voltage generation circuit to generate a reference voltage, a differential voltage signal generation circuit to convert send data input in sending data into a pair of differential voltage signals and output the pair of differential voltage signals based on the reference voltage generated by the reference voltage generation circuit, a receiver to convert a pair of differential voltage signals input in receiving data and output received data, and a receiver test circuit to perform a sensitivity test of the receiver, the receiver test circuit having a resistance circuit to generate a pair of differential voltage signals having a potential difference being necessary for the sensitivity test of the receiver.

In a described embodiment, a multi-agent system for processing information is provided including a data processing agent configured to ingest and normalize raw data inputs to produce standardized data and a standards integration agent configured to apply reporting standards into the standardized data thereby generating integrated reporting standards. The system further includes a performance alignment agent configured to align performance indicators based on the standardized data and the integrated reporting standards and an information synthesis agent configured to process narrative information from the standardized data and the integrated reporting standards. An orchestration framework configured to manage operations of the data processing agent, the standards integration agent, and the performance alignment agent to produce a regulatory repot compliant with regulatory requirements is further provided. The orchestration framework is further executable by a large language model.

The invention discloses a multi-modal data driven general report generation method and system based on a large model, and belongs to the technical field of intelligent report generation. Firstly, texts, images and sensor data related to a report theme are obtained and subjected to standardized preprocessing; analyzing the report generation instruction, and matching and querying a task modal mapping library matching modal configuration scheme according to a task demand; quantitatively evaluating the data quality of each modal, dynamically calculating the final decision weight of each modal in combination with the basic weight, and distributing the final decision weight to a corresponding processing path to form dominant, supplementary and reference data; inputting the dominant data and the supplementary data into a multi-modal model for analysis to obtain a preliminary conclusion with confidence score, performing consistency judgment, if no conflict exists, performing fusion to form a comprehensive conclusion, and if the conflict exists, combining a quality evaluation result and an arbitration rule to complete conflict judgment; and finally, inputting the comprehensive conclusion and the reference data into a large language model to generate a report text, and outputting a complete report after typesetting and proofreading.

The invention discloses an energy consumption prediction and scheduling control method based on machine learning. According to the method, operation parameters, energy consumption curves and environment disturbance data of multiple devices are collected through a distributed sensing terminal, the data are input into a pre-trained machine learning model, and a probability prediction result of future energy consumption distribution is generated. On the basis of prediction, an intervention signal is applied in an equipment safety boundary, equipment response characteristics are obtained according to the difference before and after intervention, and an energy consumption risk map is constructed by combining the equipment response characteristics with a probability prediction result. And based on the energy consumption risk map, generating an extreme disturbance scene by using digital twinning, performing consistency check on a probability prediction result and a scheduling scheme in a data domain and a physical domain, and performing multi-stage scheduling in combination with task delays to generate a scheduling result. And finally, issuing the scheduling result to the equipment. The method can improve the accuracy of energy consumption prediction and the reliability of scheduling decision making, and is suitable for intelligent management of data centers, industrial production and high-energy-consumption scenes.

The invention discloses an automatic soft soil foundation settlement monitoring system based on multi-source data fusion, and relates to the technical field of soft soil foundation monitoring, the system comprises an information acquisition module, a fusion processing module, a settlement prediction module and an intelligent monitoring module; the information acquisition module is used for acquiring foundation settlement sensing data and inputting the acquired data into the fusion processing module; the fusion processing module is used for preprocessing and integrating the collected data; the settlement prediction module is used for soft soil foundation settlement prediction; the intelligent monitoring module comprises a self-adaptive processing module and an interaction alarm module, the self-adaptive processing module is used for generating an optimization strategy, and the interaction alarm module is used for carrying out user interaction and multi-mode abnormal alarm reminding. An early warning response window is provided for engineering personnel, and the occurrence rate of sudden settlement accidents is reduced.

The invention provides an automatic energy-saving control method and device for a ring cooling fan based on load feedback, relates to the field of ring cooling fans, and solves the technical problem of delay of regulation and control in an energy-saving working state. The method comprises the steps that working condition data are input into a preset powder box model, and a feed-forward air volume instruction is obtained through calculation; and inputting the working condition data into a state observer to obtain an optimal estimated temperature value. And the deviation between the optimal estimated temperature value and a preset temperature set value is calculated, and a feedback air volume compensation instruction is obtained through calculation of a feedback controller according to the deviation. And fusing the feed-forward air volume instruction and the feedback air volume compensation instruction to obtain a final air volume control instruction, and issuing the final air volume control instruction to a fan frequency converter for execution. And continuously monitoring the numerical value and the change trend of the feedback air volume compensation instruction, taking the feedback air volume compensation instruction as a prediction error signal of the powder box model, and adaptively adjusting key thermal parameters in the powder box model. The method is used in the control process of the ring cooling fan.

The invention provides a tunnel grouting dynamic adaptive simulation method and system based on multi-physics field coupling, and belongs to the technical field of grouting simulation, and the method comprises the steps: obtaining original geological information, constructing a three-dimensional geological geometric model and a fracture network, constructing a multi-physics field coupling mechanism, and carrying out discrete solution; further predicting slurry diffusion and crack filling to obtain an isobaric envelope diagram and an early warning area, and simulating a grouting process; acquiring real-time sensor data, inputting the isobaric envelope diagram, the early warning area and the real-time sensor data into an adaptive parameter prediction model, dynamically updating the weight of the adaptive parameter prediction model according to the change of the real-time sensor data by adopting rolling training to obtain a prediction index, and constructing a feedback control chain based on the prediction index. Determining an optimal grouting parameter combination by adopting a particle swarm optimization algorithm; and the optimal grouting parameters are fed back to the simulated grouting process for automatic parameter adjustment, and the optimized grouting strategy is executed. The intelligent numerical values of the grouting parameters can be dynamically and adaptively adjusted.

The invention provides an electromagnetic field prediction method and device and electronic equipment, and relates to the technical field of electromagnetic field solving. The method comprises the following steps: acquiring historical electromagnetic original data in a field-line coupling scene, preprocessing the historical electromagnetic original data, inputting the preprocessed historical electromagnetic original data into an LSTM-PINN model, and outputting a physical field quantity mapping result; wherein the physical field quantity comprises an electric field component and a magnetic field component; setting a weighted loss function, and performing optimization training on the LSTM-PINN model based on a physical field quantity mapping result and an error of a real physical field quantity corresponding to historical electromagnetic original data to obtain a trained electromagnetic field prediction model; wherein the weighted loss function comprises a data loss function, a physical residual loss function, an initial condition loss function and a boundary condition loss function; and inputting real-time electromagnetic original data into the trained electromagnetic field prediction model to obtain a spatio-temporal distribution electromagnetic field prediction result. The method can effectively extract the spatial distribution features and the time sequence features at the same time, and is suitable for a complex field-line coupling problem.

The invention discloses a multi-source-domain multi-teacher knowledge distillation method and system based on reinforcement learning, and the method comprises the steps: obtaining target domain sample data, inputting the data into N pre-trained teacher models, and generating the output features of all teacher models; inputting the target domain sample and all teacher model outputs into a reinforcement learning strategy network, generating a dynamic weight of each teacher model, and calculating a knowledge distillation loss function based on the dynamic weights; constructing a total loss function according to the knowledge distillation loss function and the cross entropy loss output by the student model; student model parameters are updated through gradient descent; and calculating a reward value according to student model performance change, and updating reinforcement learning strategy network parameters. According to the method, the reward function based on student model performance improvement is constructed, the strategy network is continuously updated in a strategy gradient optimization mode, the distillation efficiency is effectively improved, knowledge conflicts among teachers are relieved, and the robustness and generalization performance of the student model in a multi-source complex environment are remarkably improved.

The invention discloses an SF6 gas leakage detection method and system based on a photoacoustic spectrum analyzer, and the method comprises the steps: arranging a sampling end to collect an SF6 gas sample, and obtaining stable gas input through constant-current sampling and steady-state pretreatment; steady-state gas is guided into the photoacoustic spectrum analyzer, and resonance frequency stabilization and signal amplification output are achieved through the self-tuning unit; executing double-microphone differential detection and digital filtering processing, and outputting a stable SF6 detection signal with a high signal-to-noise ratio; performing time calibration, abnormity elimination and consistency processing on the detection signal to generate standardized detection data; inputting the standardized data to a Transform model, and performing inversion to generate an SF6 leakage source position and a diffusion path; and displaying an inversion result on a monitoring interface, triggering a sound-light alarm when the inversion result exceeds a limit, and uploading the inversion result to a cloud monitoring platform. According to the invention, the intelligent photoacoustic spectrum system combining photoacoustic-fluid steady-state control and self-tuning detection is constructed, so that high-sensitivity detection and accurate positioning of SF6 gas leakage are realized.

The invention relates to the technical field of large model intention understanding, and discloses an APP dialogue type service reaching method and system based on large model intention understanding. The method comprises the steps of receiving a natural language text input by a user on an APP dialogue interface and performing large model semantic analysis to obtain service semantic data; inputting the business semantic data into an intention recognition model for business intention understanding to obtain business intention data; performing multi-agent cooperative process planning to obtain execution process data; carrying out interactive confirmation on the service parameters to obtain service execution parameters; transmitting the service execution parameters to corresponding service tool interfaces for calling execution to obtain a service tool calling result, and performing intelligent analysis and card rendering on the service tool calling result to obtain display card data. According to the method, the real business intention of the user is accurately understood, and the problems of execution efficiency and stability of a traditional single interface calling mode in a complex business scene are solved.

The invention relates to a big data-based passenger-roll transport demand prediction and ship intelligent scheduling method and system. The method comprises the steps of obtaining multi-source shipping data for a target area; inputting the multi-source shipping data into the spatial-temporal feature mining model, and predicting passenger rolling transportation demand information of the target area; acquiring ship real-time position, passenger carrying capacity, energy consumption data and port real-time operation state in the target area, and dynamically generating an optimal scheduling scheme by adopting a shipping scheduling model in combination with the predicted passenger transport demand information; the shipping scheduling model is obtained by interacting a decision scheduling model with an intelligent agent corresponding to the passenger roller transportation system and performing iterative training by adopting a reinforcement learning algorithm; and converting the optimal scheduling scheme into visual information, pushing the visual information to operation terminals of the ship and port workers so as to start corresponding shipping scheduling operation, and monitoring the execution effect of the shipping scheduling operation in real time.

The invention discloses a method and system for predicting leakage of a water supply pipe network, and the method comprises the steps: modeling nodes and pipe sections of the pipe network into a graph topological structure, and endowing the nodes and the pipe sections with static attributes; collecting operation data of the water supply network, and constructing time-varying graph data corresponding to the graph topology; combining the time-varying graph data with the static attributes to form space-time input features; constructing a graph time sequence prediction model based on a deep learning framework, performing graph structure feature extraction on node graph features and pipe section graph features of each time step to obtain node space features and pipe section space features, and outputting a node and pipe section space-time representation set; evaluating and analyzing the leakage level of each DMA or pressure partition; generating a pipe section leakage risk space distribution set; constructing a joint loss function, and training and updating the graph time sequence prediction model; and inputting operation data acquired in real time into the trained graph time sequence prediction model, and generating a leakage rate prediction value of each partition and a leakage risk index of each pipe section on line for leakage prediction and operation and maintenance decision.

The invention relates to a spare part service life prediction method, device and equipment and a computer readable medium. The method comprises the steps of collecting multi-mode state data of a target spare part; verifying the historical consistency of the multi-modal state data; and under the condition that the historical consistency verification of the multi-modal state data is passed, inputting the multi-modal state data into a target residual life prediction model so as to predict a degradation track of the target spare part based on the multi-modal state data by using the target residual life prediction model, the target residual life prediction model is a neural network model obtained by training by taking a physics degradation mechanism of the spare part as priori knowledge; and determining the predicted remaining life of the target spare part based on the degradation trajectory. According to the method, the evaluation one-sidedness caused by insufficient single data dimension is avoided, the prediction credibility is improved through a data verification and physical mechanism constraint model, and the technical problem of low life prediction accuracy caused by spare part life counterfeiting is effectively solved.

The invention discloses a power equipment defect detection method based on image recognition, and belongs to the technical field of power equipment defect detection, and the method comprises the steps: carrying out the defect simulation based on physical mechanism driving according to an equipment three-dimensional model and physical field simulation parameters, and obtaining a defect simulation data set; according to the defect simulation data set and the real inspection data, training a cross-modal deep learning network based on physical law constraint to obtain a defect identification model; performing time-space diagram neural network modeling according to the historical time sequence inspection data and the defect identification model to obtain a state evolution model; and inputting inspection data acquired in real time into the equipment health state evolution model, and performing online reasoning to obtain a defect detection result. The problems that an existing electrical equipment defect detection method excessively depends on scarce real defect samples, the generalization ability for complex working conditions is weak, and the defect evolution trend prediction ability is lacked are solved.

The invention relates to the technical field of bridge health monitoring, and discloses a method for judging rigidity change of a bridge structure based on bridge health monitoring deformation data. The method comprises the following steps: establishing an initial data set of bridge deformation monitoring data and performing multi-scale decomposition processing to generate deformation component data of different time scales; inputting the deformation component data of different time scales into a pattern recognition engine, and recognizing a characteristic pattern data stream associated with the structural rigidity; constructing a rigidity influence factor sequence based on the characteristic mode data flow, and calculating a statistical characteristic quantity of the rigidity influence factor sequence through a sliding time window; performing multi-dimensional matching analysis on the statistical characteristic quantity and a historical reference database, and outputting a stiffness anomaly probability index; and activating a hierarchical verification mechanism according to the stiffness anomaly probability index, and confirming a stiffness change trend through a cross validation algorithm. Reliable data support is provided for bridge structure health condition evaluation.

The invention discloses a slope displacement prediction method, device and equipment and a storage medium, and the method comprises the steps: collecting displacement data and multi-source environment factor data of a to-be-monitored slope region, and carrying out the preprocessing of the displacement data and the multi-source environment factor data; performing time-frequency decoupling on displacement data by adopting a variational mode decomposition method to obtain a plurality of mode components, and merging the mode components with the multi-source environment factor data to obtain an input matrix; introducing a supervision loss function into the constructed initial prediction neural network model, and training based on an error feedback mechanism and the input matrix to obtain a time sequence prediction neural network model; and inputting monitoring data of a slope area to be monitored into the time sequence prediction neural network model, and generating a complete future displacement prediction sequence through inverse mode reconstruction. According to the method, the problems in the prior art are solved from data acquisition and processing, feature analysis, model training and prediction output, and the accuracy and reliability of slope displacement prediction are improved.

The invention provides a reservoir bank slope deformation body instability volume prediction method, which comprises the following steps: respectively acquiring earth surface displacement and rock mass internal deformation data through a millimeter wave radar and a tilt angle sensor, and after processing through an adaptive noise decomposition algorithm, identifying a key deformation area and generating a data set. And performing space-time alignment on the data by using the engineering coordinate system and the topological relation to generate a fusion matrix. And reconstructing a potential slip crack surface geometric model in combination with slip crack surface features of historical cases, and calculating instability volume probability distribution by adopting Monte Carlo simulation. And finally, inputting the multi-dimensional data into the space-time prediction model, and outputting an instability volume prediction result with probability distribution. According to the invention, the accuracy and reliability of the prediction result can be improved, and scientific basis and technical support are provided for safety monitoring and disaster early warning of the reservoir bank slope.

A crop growth modeling system includes a memory configured to store computer-readable instructions. The instructions cause to the system to use a digital twin component configured to create and manage a digital twin of a farm. The instructions cause to the system to use a data input component configured to receive data related to a defined set of land characteristics and environmental attributes for the farm. The instructions cause to the system to use a processing component configured to integrate the received data with the digital twin and to simulate at least one crop growth scenario based on the integrated data. The instructions cause to the system to use a prediction component configured to determine a predicted crop growth rate for the farm based on the simulations conducted by the processing component.

The invention provides a large model intelligent reasoning method combining reinforcement learning and retrieval enhancement generation, and belongs to the technical field of artificial intelligence. Comprising the following steps: data input: data preprocessing; constructing a reinforcement learning environment; an RAG mechanism is integrated; training the model; and evaluating and iteratively optimizing. A reinforcement learning framework based on rules is introduced to guide a model to develop advanced reasoning skills such as reflection, verification and summarization. And in combination with a retrieval enhancement generation mechanism, the model can access and utilize a wide background knowledge base before answering questions. The synergistic effect between information retrieval and text generation is optimized. According to the method, the deficiency of knowledge of the model can be made up by introducing the external knowledge base, and the synergistic effect between information retrieval and text generation can be optimized in the reinforcement learning process, so that the capability of the model for processing complex reasoning tasks is remarkably improved, and formation of a more generalization reasoning strategy is promoted.

The invention discloses a medical diagnosis auxiliary method and system based on thinking chain visualization, and belongs to the technical field of medical auxiliary diagnosis, and the method specifically comprises the steps: obtaining the related data of a patient, generating an initial diagnosis thinking chain, detecting and recognizing a target sub-chain node needing to be updated based on the graph structure difference when new medical data is received, and updating the target sub-chain node according to the target sub-chain node. Performing local increment updating on the target sub-chain, visualizing the updated thinking chain, and dynamically updating difference detection parameters and optimizing the thinking chain according to the backtracking operation or annotation data of the reasoning node; according to the method, the calculation burden and the graphic rendering overhead are reduced, frequent jitter or breakage of the chain structure is avoided, the response efficiency and the structural stability of the system to multi-stage data input in a complex diagnosis and treatment process are remarkably improved, the transparency and the understandability of reasoning logic are enhanced, and intelligent auxiliary diagnosis better meets the actual clinical requirements.

The invention discloses a multi-modal perception and optimization method and system for a low-power-consumption AR device, and the method comprises the steps: collecting multi-modal data, task demands, resource state data and environment data for the AR device; lightweight processing is carried out on the multi-modal neural network model through model pruning, parameter quantification and distillation technologies; inputting the collected data into a lightweight multi-modal neural network model for dynamic reasoning to obtain a multi-modal recognition result; comprising the steps of executing modal adaptive weight acquisition based on task requirements and environment data; executing energy consumption constraint scheduling according to the equipment resource state data, dynamically selecting a reasoning path strategy, and obtaining corresponding modal feature output; multi-modal feature fusion is carried out, task reasoning is completed, and a multi-modal recognition result is obtained; early-leaving control is executed based on a middle-layer confidence coefficient threshold value in the reasoning process; and interactively outputting a real-time multi-mode identification result. According to the invention, energy efficiency and precision balance and multi-mode fusion low-power-consumption optimization can be realized.

The invention discloses a multi-source data fusion foam concrete construction process monitoring method and system, and relates to the technical field of concrete construction, the method comprises the following steps: obtaining a standard response data set of foam concrete, including raw material ratio parameters and performance index parameters which are stored in an associated manner; constructing and training a hybrid hierarchical performance prediction model, wherein the model comprises a shared prediction layer based on integrated learning and a plurality of independent output layers connected with the shared prediction layer; in combination with a multi-objective optimization algorithm, by taking compressive strength and cost optimization as an objective, constructing an evaluation function for global optimization, and obtaining a target mix proportion scheme; acquiring real-time process data associated with the target mix proportion scheme, and inputting the real-time process data into the mixing layering performance prediction model to obtain a prediction result; and comparing with a preset design target, and generating an adjustment instruction for adjusting the matching parameters of the subsequent stirring batches. The problem that in the prior art, the performance fluctuation of foam concrete of different batches is large due to the fact that the construction condition cannot be dynamically adjusted in real time is solved.

The invention belongs to the technical field of spacecraft pose estimation, and particularly relates to a cross-domain spacecraft pose estimation method based on mask self-distillation domain adaptation, and the method comprises the steps: 1, inputting a complete image, and employing a Faster R-CNN algorithm to position a spacecraft bounding box; the robustness of the model is improved by applying a track environment data enhancement strategy; and extracting a target ROI region as key point regression network input based on the detection frame. 2, dividing ROI (Region of Interest) data of a source domain and a target domain; and optimizing heat map supervision loss learning key point positioning knowledge. 3, inputting random mask enhanced target domain data into the student model; inputting original target domain data into the teacher model; a learnable shared prototype space is constructed, and self-distillation is guided through heat map consistency loss and semantic consistency loss. And 4, jointly optimizing the loss of the key point regression network 3, and realizing progressive migration of source domain annotation knowledge to a target domain. And 5, solving the 6D pose of the spacecraft relative to the camera through the EPnP. According to the invention, robust six-degree-of-freedom pose estimation of the target spacecraft is realized.

The invention discloses a mountain flexible photovoltaic support service state evaluation method and device, and aims to improve the operation safety and operation and maintenance efficiency of a photovoltaic power station in a complex mountain environment. Comprising the steps of establishing a digital twinborn model of the flexible photovoltaic support, deploying a multi-source heterogeneous sensor system, and collecting environmental load, structural response, material state and geological environment data in real time. Collected data are subjected to preprocessing, feature extraction and operation condition recognition, the data are input into a digital twinborn model for multi-physics coupling simulation analysis, and model parameters are inversed and corrected based on real-time monitoring data. And finally, based on the optimized digital twinborn model and real-time data, carrying out comprehensive evaluation on structure health, material aging, anchoring stability, performance degradation and the like, and generating risk early warning and maintenance strategy suggestions according to evaluation results. According to the method, the limitation of a traditional evaluation method in application of the mountain flexible photovoltaic support is overcome, and comprehensiveness, real-time performance and intelligentization of evaluation are achieved.

The invention relates to the technical field of soil environment monitoring, and discloses a soil remediation construction site environment monitoring method based on edge computing cloud collaboration. The method comprises the following steps: deploying an edge computing node at a restoration construction site, and collecting real-time data of soil humidity, heavy metal concentration, volatile organic compound content and meteorological parameters through a multi-source sensor; inputting the data into a spatio-temporal feature extraction network, and generating a spatio-temporal fusion feature matrix by means of hierarchical convolution and a cross-channel attention mechanism; on the basis of the matrix, the index anomaly probability is calculated by using a depth probability network, and a risk feature sequence with uncertainty measurement is generated; constructing a dynamic risk field model in combination with repair process parameters, and predicting a pollution diffusion path through a space-time propagation algorithm; and in combination with an equipment operation state, an edge side local regulation and control instruction and a cloud global optimization strategy are generated by a collaborative decision engine, and efficient and accurate environment management requirements of a construction site are met.