337 results about "Real time data acquisition" patented technology

The invention discloses a CPLD-driven IGBT short-circuit current dynamic monitoring method and system, and relates to the technical field of power electronics, and the method comprises the specific steps of S100 real-time data acquisition, S200 dynamic threshold calculation, S300 short-circuit state judgment, S400 rapid protection execution, and S500 fault data recording and uploading. Accurate and prospective judgment of the short circuit state of the IGBT is achieved, two stress parameters including the direct-current bus voltage and the junction temperature serve as independent variables, when the bus voltage rises, the voltage stress borne by the IGBT in the turn-off period is increased, meanwhile, the current peak at the short circuit moment is more remarkable, early warning is conducted in advance by dynamically lowering the threshold value, and therefore the short circuit state of the IGBT is accurately judged. According to the mechanism, the monitoring system does not simply respond to the occurred overcurrent, but can pre-judge the safety boundary of the device under the current working condition, so that intervention is implemented before the current really reaches a dangerous peak value, and a protection blind area under a severe working condition is effectively avoided.

The invention discloses a source network load storage oriented regional collaborative scheduling method and system, and relates to the technical field related to power resource scheduling, and the method comprises the steps: traversing target region source network load storage to carry out real-time data collection, and constructing a source network load storage multi-dimensional data set to carry out multi-scale layered optimization; operation constraint conditions are set, limitation is carried out in combination with a power grid topological structure, a cooperative scheduling instruction set is determined, cooperative control feedback is carried out, and regional power scheduling feedback parameters are generated; and multi-channel dynamic correction is carried out, and a cooperative scheduling optimization strategy is generated to carry out energy balance cooperative scheduling on the source network load storage in the target area. The technical problems that in the prior art, intermittent fluctuation of new energy is difficult to consume, the collaboration of all links of source network load storage is poor, the dispatching flexibility of a power system is insufficient, and the resource configuration efficiency is low are solved. The technical effects of optimizing regional energy resource allocation and improving the clean energy consumption level, the power grid operation stability and the source grid load storage cooperative response capability are achieved.

The invention relates to the field of digital twinborn technology and industrial equipment intelligent operation and maintenance, and discloses an electric power screen cabinet heat dissipation efficiency improvement and thermal management comprehensive analysis system, which comprises an offline modeling module for constructing a thermal order reduction model with efficient calculation through a model order reduction method based on high-fidelity CFD simulation data; the real-time data acquisition module is used for acquiring real-time power consumption, sparse sensor temperature and an expected load curve; the online state estimation module is used for fusing physical prediction of the thermal reduced-order model with real-time sparse measurement data and reconstructing a global three-dimensional temperature field of the power screen cabinet in real time; and the prospective scheduling analysis module is used for taking the current global temperature field as an initial condition, driving the thermal reduced-order model to perform prospective deduction by utilizing an expected load curve, and calculating and outputting a future thermal margin to early warn a thermal risk. According to the invention, comprehensive real-time sensing, prospective risk prediction and self-adaptive correction of the thermal state of the electric screen cabinet are realized, and thermal management is converted from passive response to active prevention.

The present invention discloses an intelligent control method for sewage treatment equipment at dry bulk cargo terminals, relating to the technical field of sewage treatment equipment control, and is used to address the problem of poor control of sewage treatment equipment at terminals. The method comprises following steps: installing multiple types of sensors at key locations on dry bulk cargo terminals, utilizing edge computing nodes for real-time data acquisition and preprocessing, combining historical and temporal features with machine learning models to classify sewage types, achieving efficient dynamic adjustment of sewage treatment equipment operation parameters, then using weighted voting and confidence assessment to integrate multiple classification results to ensure optimal treatment results, and analyzing the actual sewage treatment situation to optimize equipment control continuously, thereby preventing failures, extending equipment life, and improving sewage treatment effectiveness.

The invention relates to the technical field of geological early warning, in particular to a dangerous rock mass instability analysis method, system and equipment based on a space-time diagram neural network, by fusing unmanned aerial vehicle LiDAR, multispectral data, meteorological radar data and the space-time diagram neural network (ST-GNN), the system realizes sub-meter spatial resolution and minute-level time response, and the stability of dangerous rock mass instability analysis is improved. The four-dimensional (time and space) analysis result of the instability probability of the dangerous rock mass is obtained through high-precision space-time modeling, the problems that a traditional geological disaster early warning system is low in resolution ratio, slow in response and high in misinformation are solved, the comprehensiveness, accuracy and reliability of instability prediction of the dangerous rock mass are improved, and the early warning effect is good. And full-chain intelligent closed-loop management of real-time data acquisition-dynamic prediction-early warning push-feedback optimization is supported, the emergency decision time is shortened by real-time rainfall superposition risk thermodynamic diagrams, and the attenuation rate of long-term prediction precision is reduced by dynamically fusing newly added geological data and instability events through incremental learning.

The invention relates to the technical field of wind power blade material testing, and discloses a testing device and method for a salt spray corrosion test of a wind power blade composite material layer. According to the method, wind power blade composite material coating preparation parameters are obtained, and coating basic information is provided for subsequent performance evaluation; based on preset salt spray test environment control parameters, dynamic salt spray environment simulation conditions are generated so as to better fit the actual outdoor environment; then, a multi-sensor coupling monitoring network is constructed, real-time data acquisition is conducted on the corrosion process of the composite material coating, and comprehensive tracking of the corrosion process is achieved; processing real-time data by utilizing a corrosion characteristic analysis algorithm, generating a multi-dimensional corrosion characteristic index, and quantitatively representing the corrosion condition of the coating; and finally, evaluating the anti-corrosion stability of the new material coating in combination with the coating characteristic parameters and the multi-dimensional corrosion characteristic indexes. According to the method, the corrosion resistance of the coating in the actual environment can be more accurately reflected, and support is provided for research, development and application of the composite coating of the wind power blade.

The invention discloses a building electrical safety protection method, which comprises the following steps of performing real-time data acquisition on a building electrical system to obtain multi-modal time sequence data; constructing the multi-modal time sequence data into graph structure data with a node-edge topological relation, and encoding the multi-modal original observation data corresponding to each node into a multi-modal initial feature vector of the node; graph neural network feature extraction is carried out on the graph structure data, and a global feature vector sequence used for representing the operation state of the electrical system is obtained in combination with an attention mechanism; inputting the global feature vector sequence into a pre-constructed time sequence prediction model to perform operation state prediction, and judging whether potential abnormality exists or not; and when the residual error exceeds a preset threshold value, fault backtracking positioning is carried out on the key node according to the attention weight, and an electrical fault point is generated in combination with node characteristics. According to the invention, the fault response speed and the emergency disposal efficiency can be effectively improved, and the safety accident rate is reduced.

The invention provides an electric power operation and maintenance work ticket intelligent collaborative auditing system based on a large model, and relates to the technical field of data acquisition management, and the system comprises a data acquisition module which is used for collecting related influence parameters of a work ticket; the intelligent model processing module is used for constructing an influence mathematical model and outputting an influence coefficient; the execution difficulty quantification module is used for collecting resource data required when the to-be-processed power operation and maintenance work ticket is executed, and obtaining an execution difficulty index; and the auditing output module is used for setting an auditing judgment cardinal number and judging whether manual auditing intervention is needed or not. According to the invention, the accuracy and timeliness of information are ensured through real-time data acquisition and automatic processing, and manual errors and delay are reduced; the auditing efficiency and reliability are greatly improved, the adaptive auditing decision is triggered in the rule engine, so that resource allocation is optimized, whether the work order is executed smoothly or not can be considered more fully, the work order execution smoothness is improved, and the task completion rate is increased.

The invention relates to the technical field of grouting systems, and discloses an intelligent grouting control system for a high-pressure jet grouting pile, which is characterized in that a closed-loop control system integrating real-time data acquisition, intelligent parameter regulation and control, accurate process execution and quality evaluation feedback is constructed; the construction links which originally depend on manual experience and are scattered and independent are integrated into a highly-collaborative intelligent whole. The construction state and the stratum condition can be sensed in real time, automatic decision making is carried out, the optimal process parameters are executed, the pile forming effect is evaluated in real time, feedback optimization is carried out, and therefore passivity and uncertainty of a traditional construction mode are changed fundamentally. And finally, the effect of improving the extensive and empirical manual operation of the jet grouting pile construction into a fine and intelligent modernized technological process is achieved, the uniformity, stability and reliability of the single pile construction quality are effectively ensured, and the pile forming quality level and the construction efficiency of the whole project group are continuously improved through learning iteration of historical data.

The invention relates to the technical field of power system equipment state monitoring and fault diagnosis, in particular to a GIS basin-type insulator operation state evaluation method and system, and the system comprises an asset information and baseline modeling module which is used for building a high-fidelity multi-physics field finite element reference model and generating a defect state-external representation mapping data set; the physical information driven agent model generation module is used for constructing a neural network agent model fusing physical law constraints; the real-time data acquisition and feature extraction module is used for acquiring and processing online monitoring data such as UHF, gas, temperature and vibration; the state inversion and digital twinning calibration module is used for inverting internal defect parameters by adopting a Bayesian inference and MCMC method so as to realize real-time calibration of the model; and the evaluation diagnosis and life prediction module carries out fault mode identification and residual life prediction based on the calibration model.

The invention relates to the technical field of information, and discloses a comprehensive operation supervision system based on an intelligent supply chain. The system comprises a real-time data acquisition module, a multi-modal feature extraction module, a dynamic risk assessment module, a self-adaptive decision generation module and an execution feedback regulation and control module. By constructing a real-time data acquisition and multi-modal feature extraction module, efficient fusion and deep feature extraction of internal and external multi-source heterogeneous data of the supply chain are realized, the sensing sensitivity and accuracy of the system to market fluctuation and emergencies are remarkably improved, and the prediction deviation is reduced from the data source. The dynamic risk assessment module adopts a dual anomaly detection mechanism, various potential risks can be quickly and comprehensively identified, risk level quantification is carried out in combination with a time sequence mode, and accurate and timely risk situation assessment is provided for subsequent decision making.

The invention provides a multi-mode fusion laser directional energy deposition real-time monitoring method and system, and belongs to the technical field of additive manufacturing. Comprising the steps that real-time data collection is conducted on a molten pool area in the laser directional energy deposition process through synchronous triggering of a multi-source sensor; a distributed clock network is constructed, environment influence parameters are collected in real time in combination with a working condition sensing unit, dynamic modeling and correction are conducted on asymmetric link delay in the clock synchronization process, and space-time alignment of multi-source sensor data is achieved; after the multi-source sensor data subjected to space-time alignment is preprocessed, data-level fusion, feature-level fusion and decision-level fusion are performed in sequence. The state of the molten pool can be accurately monitored in real time, and the quality and stability of the laser directional energy deposition process are improved.

The invention relates to the technical field of cigarette production, in particular to an intelligent prediction method and system for a cigarette mixed flow production line and a storage medium, and the method comprises the steps: obtaining cigarette production inventory information in real time; setting a dynamic early warning line according to the cigarette production inventory information; adopting a machine learning model to predict the productivity of the wrapping equipment; predicting a process plate changing time point sequence; predicting the production progress of cigarettes of different brands; predicting the total production time required for executing the current cut tobacco production scheduling plan; judging whether the total time meets requirements or not; under the condition that the total time meets the requirement, the inventory in the next period is predicted according to the current production scheduling plan; and under the condition that the total time does not meet the requirement, increasing the constraint condition of the total time, and returning to execute the step of predicting the procedure card changing time point sequence according to the dynamic early warning line and the productivity of the rolling and packing equipment. Real-time data acquisition, big data analysis and an intelligent algorithm are deeply fused, so that intelligent management of the whole process of the production line is realized, and the production efficiency is improved.

The invention discloses a lightning fire occurrence risk grade forecasting method and device and a storage medium. Belongs to the technical field of forest fire prediction. The method comprises the following steps: acquiring hour-scale real-time data of a target area; extracting a real-time numerical value corresponding to each key factor in a predefined key factor set from the real-time data; inputting the real-time numerical values corresponding to all the key factors into a pre-trained risk level prediction model to obtain a lightning fire occurrence probability corresponding to each lightning in the target area; and according to the occurrence probability of the lightning fire, determining the risk level of the lightning fire caused by each lightning in the target area. Therefore, scientific screening of key factors of lightning fire forecasting is realized, hour-scale real-time data acquisition is adopted, and data processing and model forecasting logic adapted to hour-scale dynamic change are adopted, so that forecasting accuracy and real-time performance are effectively improved.

The invention relates to the technical field of pile foundation testing, in particular to a test pile load testing method and a loading structure, and the method comprises the following steps: testing preparation: establishing a real-time data acquisition link; graded loading: graded loading: collecting pile top settlement-time sequence data in real time at the load holding stage of the current load stage; self-adaptive load holding judgment: calculating a settlement convergence trend based on settlement data acquired in real time under the current load level; if the prediction model judges that the settlement is fully converged under the current load level, directly entering the next load level without waiting for the settlement rate to be reduced to a fixed threshold value; if the prediction shows that the settlement cannot be converged or tends to be damaged, triggering damage early warning and turning to a damage treatment flow; continuously detecting, namely repeating the step of graded loading and the step of self-adaptive load holding judgment until a preset maximum test load is reached or damage is triggered; and evaluation: evaluating the bearing capacity of the pile foundation. The test efficiency is improved, and the intelligent degree is improved.

The invention relates to the technical field of water treatment, in particular to a water plant process modularization deconstruction integration system, which comprises a physical module layer, a water treatment layer and a control layer, the edge intelligent control layer is connected with the physical module layer and is used for real-time data acquisition and bottom layer control; the digital twin platform layer communicates with the edge intelligent control layer and is used for constructing a high-fidelity virtual model for the physical module layer and realizing virtual-real interaction; the cloud platform AI center communicates with the digital twin platform layer and is used for operating an artificial intelligence optimization algorithm; and through self-adaptive optimization of the DRL intelligent agent, accurate control of key process parameters (such as dosage and aeration rate) can be realized, and it is predicted that the drug consumption can be reduced by 10%-25% and the aeration energy consumption can be reduced by 15%-30%.

The invention relates to the technical field of storage space dynamic management, in particular to a storage space dynamic management system based on multi-source data fusion and intelligent optimization. Comprising a real-time data acquisition and fusion module, a dynamic demand prediction and visualization module, a multi-target dynamic optimization decision module, a scheme decision and instruction execution module and a closed-loop feedback and model updating module, the dynamic demand prediction and visualization module predicts demands based on the data set and generates a dynamic demand thermodynamic diagram, the multi-target dynamic optimization decision module constructs a multi-target goods allocation model and adaptively adjusts the weight to solve an optimal scheme set, and the scheme decision and instruction execution module selects a scheme and analyzes the scheme into an operation instruction; and the closed-loop feedback and model updating module forms a closed loop and updates the prediction model. According to the invention, dynamic intelligent optimization of the storage space is realized, the operation efficiency is improved, the management cost is reduced, and modern storage management requirements are met.

The invention discloses a method for improving data acquisition / writing stability, and relates to the technical field of industrial control systems. The method comprises the following steps: firstly, deploying a real-time operating system kernel on general computing hardware, and carrying out real-time transformation on a hardware interface driving program of the real-time operating system kernel so as to lay a deterministic response foundation; secondly, real-time adaptation is carried out on an engine when the container runs, so that the engine has the capability of calling a kernel real-time interface; meanwhile, a non-real-time data acquisition / write-in program is packaged in a standard container mirror image. And finally, through the adapted container runtime engine, creating and running the container instances on the real-time kernel, thereby ensuring the deterministic execution of key real-time tasks on a unified hardware platform, and flexibly deploying non-real-time tasks through a containerization technology to achieve the comprehensive target of efficient utilization of resources and performance isolation.

The invention discloses an open-pit mine slope deformation stage judgment method based on GNSS monitoring data, and belongs to the technical field of open-pit mine slope disaster prevention and control. According to the invention, dynamic perception of the whole slope deformation process is realized by combining multipoint GNSS monitoring real-time data acquisition and an intelligent processing algorithm. Compared with a traditional mode depending on single-point displacement or intermittent observation, the method has the advantages that trend change in the slope deformation process can be effectively captured by building a dynamic model of the displacement rate angle and the slope of the displacement rate angle, particularly, the abnormal state can be recognized in advance in the accelerated deformation stage, the sensitivity and accuracy of deformation stage judgment are remarkably improved, and the method is suitable for popularization and application. The method is suitable for complex working conditions of strip mines; the method for analyzing the displacement rate angle and the slope A value of the displacement rate angle is compatible with equal-interval and non-equal-interval sampling data, so that the algorithm has good universality and field adaptability, and the analysis requirements under different monitoring devices and data sampling frequencies are met.

The invention discloses a surrounding rock damage characterization method and device and a storage medium, and relates to the technical field of geotechnical engineering and underground engineering construction. The surrounding rock damage characterization method comprises the following steps: collecting physical monitoring parameters and real-time geological parameters of surrounding rock in a construction period; based on the real-time geological parameters and the physical monitoring parameters, obtaining a surrounding rock plastic zone range through numerical simulation inversion; corresponding sub-damage indexes are calculated according to the physical monitoring parameters and the surrounding rock plastic zone range; based on a pre-trained AI weight distribution model, inputting real-time geological parameters, and outputting a dynamic weight of each sub-damage index; calculating a comprehensive damage index according to each sub-damage index and the dynamic weight of the sub-damage index; and grading the surrounding rock damage state according to the comprehensive damage index to obtain the surrounding rock damage state. According to the method, a whole-process closed loop from multi-source real-time data acquisition, coupling analysis to damage grade judgment and feedback is realized, the method fits the actual scene of chamber construction and operation, and the real-time performance and the accuracy of evaluation are improved.

The invention discloses an airflow organization optimization design system and method based on a clean room tuyere, and relates to the technical field of clean room tuyere design, the system comprises a digital twin model construction module, a real-time data acquisition module, a problem identification module, a parameter simulation optimization module and a pre-debugging scheme output module; according to the method, the digital twin model of the clean room is constructed through computer-aided design and linked with real-time operation data, conversion of airflow organization optimization from static design to dynamic response is achieved, the problem that in the prior art, an optimization scheme is disjointed with an actual operation scene is solved, and by automatically recognizing problematic tuyeres and simulating and optimizing a parameter adjustment scheme, the optimization efficiency is improved. Traditional blind debugging depending on manual experience is replaced, repeated trial and error are avoided, the debugging accuracy and efficiency are improved, stable maintenance of cleanliness indexes of the clean room is guaranteed, meanwhile, the influence of the debugging process on the production progress is reduced, and powerful support is provided for efficient and reliable operation of the clean room.

The invention discloses a multi-modal data fusion slow-sugar-rising baking intelligent control system and method, and relates to the technical field of food processing intelligent manufacturing, and the system comprises a data collection module, a core control module and an execution adjustment module. The method comprises the steps of initialization setting, real-time data acquisition and fusion, quality prediction and decision making, closed-loop accurate regulation and control and intelligent discharge judgment, dynamic regulation and control of multi-modal data fusion and machine learning driving are achieved, the baking process can be monitored in real time, equipment parameters can be accurately controlled, the product quality and functional components can be balanced, manual dependence is reduced, and the production efficiency is improved. And the production automation and the product consistency are improved, and the method is suitable for industrial production of slow-sugar-rising composite cereal baked food.

The present invention specifically relates to the field of aluminum plating machines. Disclosed is an efficient and environmentally friendly aluminum plating machine device having a waste heat recycling function. The aluminum plating machine device comprises a motor unit, an aluminum plating tank, an aluminum foil supply system, a cooling system, a waste heat recycling device, a control system, a power supply system, and a communication module. By means of real-time data acquisition and intelligent dynamic adjustment of working parameters of a heat exchanger and on the basis of a heat recovery error, a heat exchange area and a medium flow rate are automatically adjusted by using a PID controller, and an intelligent heat distribution system based on process requirements ensures that the required heat is preferentially obtained in each process link. The device improves waste heat recycling efficiency, reduces energy consumption, optimizes resource utilization, ensures efficient and stable operation of a production process, and solves the problems in the prior art of low heat recovery efficiency and non-uniform resource utilization.

The invention relates to the technical field of coke oven operation control, in particular to an expansion pressure detection method applied to an electric heating coke oven, which comprises the following steps: dividing coking stages based on coal state change in a historical coking period of the electric heating coke oven, and setting a reference acquisition frequency for each coking stage; for each coking stage, real-time data acquisition is carried out based on the reference acquisition frequency, and expansion pressure detection data is obtained; performing pressure anomaly calculation on the expansion pressure detection data to obtain an expansion pressure change rate and an expansion pressure distribution characteristic; correcting the reference acquisition frequency according to the reference acquisition frequency to obtain a dynamic acquisition frequency corresponding to the coking stage; and generating expansion pressure detection alarm information in response to the condition that at least one of the expansion pressure detection data, the change rate, the distribution characteristic and the dynamic acquisition frequency exceeds a corresponding preset warning threshold value. Acquisition frequency can be dynamically adjusted along with coal expansion characteristics, timeliness of pressure peak capture is guaranteed, and redundant data in a stable stage is reduced.

The invention provides a thermal runaway sensing and inhibiting method and system for an energy storage system and a medium, and the method comprises the steps: carrying out the real-time data collection of the interior of the energy storage system based on a multi-type sensor, obtaining multi-source collection data, carrying out the fusion processing of the multi-source collection data, and obtaining fusion data; constructing a real-time state vector of the energy storage system based on the fused data, and performing feature extraction and trend analysis on the state vector based on a multivariable discrimination algorithm to obtain state information of the energy storage system; judging whether to enter a thermal runaway evolution channel or not based on the function state information of the energy storage system, outputting a risk level, dynamically matching an intervention measure, and transmitting the risk level and the matched intervention measure to a terminal in real time for monitoring; according to the invention, accurate identification is carried out through multi-dimensional parameters at the initial stage of thermal runaway, a sensing-discrimination-suppression closed-loop safety control mechanism is realized, the control response is rapid, intervention measures can be rapidly started, the overall thermal safety level of the system is improved, and the accident rate is reduced.

The invention relates to an edge calculation-based equipment fault diagnosis method and system for a battery swap station. The method comprises the following steps of: acquiring data and constructing a regional equipment model; deploying the regional equipment model at an edge computing node, and collecting operation time sequence data in real time; obtaining expected state data through the lightweight simulation model, and comparing the expected state data to generate a residual sequence; obtaining a causal violation event set through a lightweight causal graph model; generating a fault diagnosis result through the fault mode map; in conclusion, the regional equipment model comprising the lightweight causal graph model, the simulation model and the fault mode graph is constructed, real-time data acquisition and closed-loop correction are realized in combination with the edge computing nodes, and the dynamic threshold optimization and fault mode graph fusion mechanism is utilized. The problems that a traditional method is large in response delay, high in false alarm rate and insufficient in diagnosis precision are effectively solved, and the effects of improving fault diagnosis real-time performance, reducing cloud data transmission delay, improving diagnosis precision and reducing the false alarm rate are achieved.

The invention, which relates to the technical field of medical training, discloses a multi-sensory feedback fusion stroke rehabilitation exoskeleton robot training system comprising an exoskeleton robot main body, a multi-sensory feedback module, and a motion state monitoring module. By integrating a multi-sensory feedback module, a visual, auditory, tactile and motion state monitoring module and a central control module, multi-dimensional sensory stimulation and real-time data acquisition are realized when a patient is trained, and the central control module adapts parameters in the module according to the real-time motion precision of the patient and personalization. The strength and the type of multi-sensory feedback are dynamically adjusted, meanwhile, motion parameters of an exoskeleton robot body are controlled, a dynamic adjustment mechanism ensures pertinence and effectiveness of training, forward feedback is provided to strengthen a correct motion mode when the motion precision of a patient is high, and correction prompt is provided and assistance or resistance of the robot is adjusted when the motion deviation is large. Therefore, nerve function remodeling and motor ability recovery are effectively promoted.

The invention discloses a tunnel construction robot path planning method and system based on deep reinforcement learning, and relates to the technical field of tunnel construction automation and robot navigation, and the method comprises the steps: pre-defining a state space and an action space of robot path planning in a tunnel construction environment, and building a dynamic reward function; constructing a deep reinforcement learning architecture based on a lightweight action network and a comment network, and training an action-comment network based on state-action pair data in an experience pool by taking the sum of an accumulated reward and an entropy regularization item of a maximization strategy as a target; and on the basis of the current state information of the tunnel construction robot, the optimal moving action of the next step is obtained by utilizing the trained action network, and path planning of the robot is realized. According to the method, the deep reinforcement learning technology is applied to the tunnel construction scene, and the autonomous decision-making ability and the path planning precision of the robot in the dynamic construction environment are optimized and improved in combination with real-time data acquisition and processing of the complex tunnel environment.

The invention belongs to the technical field of leakage detection, and particularly relates to an existing building basement side wall leakage three-dimensional detection system and method based on an intelligent wall-climbing robot. The detection system comprises an intelligent wall-climbing robot platform, a multi-modal detection sensor group, a data processing and analysis system and a user interaction terminal, wherein the multi-modal detection sensor group is carried on the intelligent wall-climbing robot platform; the intelligent wall-climbing robot platform comprises a moving adsorption system, a moving mechanism, a positioning navigation system, a posture adjusting mechanism and a communication power supply device. The multi-modal detection sensor group comprises an infrared thermal imager, a high-resolution visible light camera, a 3D laser scanner, a wall penetrating radar, a capacitive humidity sensor and an ultrasonic thickness gauge; the data processing and analysis system comprises an edge computing unit and a cloud analysis platform; the edge calculation unit is responsible for real-time data acquisition, preprocessing, sensor control and motion control. According to the existing building basement side wall leakage three-dimensional detection system and method based on the intelligent wall-climbing robot, full-thickness detection coverage from the surface layer to the deep layer, conversion from manual operation to full-automatic wall climbing and crossing from single-point detection to path tracking are achieved.

The invention discloses a vibration suppression and stability control method in the movement process of an industrial robot, and relates to the field. The method comprises the following steps of S1, constructing a robot motion digital twinborn body, S2, performing offline simulation optimization and parameter library construction, S3, performing online real-time data acquisition and transmission, S4, performing digital twinborn body comparison and parameter calling, and S5, performing closed-loop control execution and parameter iterative updating. According to the method, the digital twinborn body comprising the robot body model, the load model and the environment model is constructed, accurate optimization of control parameters under different working conditions is achieved by combining off-line simulation optimization and parameter library construction, the online real-time data acquisition and transmission system performs noise reduction processing on heterogeneous data through a multi-source information fusion algorithm, and the real-time performance of the system is improved. According to the method, the accuracy of data is ensured, a digital twin comparison and parameter calling mechanism can quickly call or generate an adaptive optimal control parameter when the vibration of the physical robot exceeds a threshold value, and the vibration is effectively inhibited.