925 results about "Compensation algorithm" patented technology

The invention relates to the technical field of automation, in particular to an automatic monitoring and optimizing system for a fine chemical production process, which comprises the following steps of: extracting time-frequency domain fusion characteristic quantity of stirring torque power time sequence fluctuation data in real time through an incidence matrix construction module, dynamically inverting a thixotropic index by combining a deep neural network model, and optimizing the stirring torque power time sequence fluctuation data; the problem that a traditional method is difficult to perceive material rheological characteristics in real time is solved. The dynamic coupling analysis module analyzes the material viscosity change rate based on the thixotropic index, fuzzy PID control is adopted to generate a stirring speed adjusting instruction dynamically matched with the viscosity and a jacket temperature compensation value, and the defects of uneven mixing and local overheating caused by lagging adjustment of process parameters are overcome; the multi-target collaborative optimization module locks the mass optimization weight in the viscosity sudden change stage, rapidly stabilizes the reaction condition through a feed-forward compensation algorithm, dynamically balances the stirring power consumption and the heat transfer efficiency based on Pareto frontier search in the steady state stage, and solves the conflict between the mass and the energy efficiency target.

The invention provides a control method of composite copper foil thickness control equipment, which comprises the following steps: calculating the deposition thickness of each abnormal concentration group at a future moment according to a deposition rate change rule diagram to obtain the copper layer thickness distribution of an abnormal region, and comparing the copper layer thickness distribution of the abnormal region with the thickness distribution of a normal region to obtain the thickness distribution of the abnormal region. Calculating a thickness deviation value of the abnormal area; according to the thickness deviation value, a deviation compensation algorithm is adopted, electroplating process parameters of the abnormal area are adjusted according to the size and the sign of the deviation value, and the electroplating process parameters comprise the current density and the electrolyte flow speed; and according to the optimized electroplating process parameters, performing parameter setting and adjustment on a power supply system and an electrolyte circulation system of the composite copper foil thickness control equipment, so that the composite copper foil thickness control equipment performs production according to the optimized electroplating process parameters.

The invention relates to the technical field of ship propulsion, and discloses an energy-saving control method for a pre-rotating guide wheel of a ship propulsion system and a fluid optimization system. According to the invention, through a multi-dimensional cooperative control strategy and fluid structure optimization, an integrated system with dynamic prerotation compensation, self-adaptive flow regulation and vortex suppression functions is constructed. The control method comprises a multi-physical field sensor array, a guide wheel angle dynamic compensation algorithm and an energy efficiency optimization decision model, a deep learning algorithm is adopted to establish a mapping relation between a propeller wake flow field and guide wheel parameters, and 0.1-second-level dynamic response is achieved. According to the fluid optimization system, an asymmetric variable-curvature guide vane structure is innovatively designed, the optimal space distribution of guide vanes is determined through three-dimensional flow field simulation, and a flow guide channel with the pressure gradient self-adaptive characteristic is formed. A real ship verifies that the propulsive energy consumption can be reduced by 10%-25%, the propulsive efficiency can be improved by 15%-30%, the cavitation effect is effectively restrained, vibration noise is reduced, and remarkable economic benefits and environmental protection values are achieved.

The embodiment of the invention relates to the technical field of data processing, in particular to an intelligent monitor temperature drift correction method and system based on a temperature compensation algorithm, and the method comprises the steps: collecting the environment temperature data of an environment where an intelligent monitor is located in real time, and obtaining a preset sensor aging factor in the intelligent monitor; based on the environment temperature data and a preset temperature-drift characteristic model, the temperature drift characteristic of the intelligent monitor is extracted, and a nonlinear compensation curve matched with the temperature drift characteristic is dynamically generated; performing iterative optimization processing on compensation parameters of the nonlinear compensation curve by adopting a self-adaptive compensation parameter optimization algorithm, and performing dynamic correction on the compensation parameters by fusing sensor aging factors in the iterative optimization processing process to obtain an optimized compensation parameter set; and performing real-time correction processing on original measurement data of the intelligent monitor according to the optimized compensation parameter set, and outputting a target measurement result after temperature drift noise suppression.

The invention discloses a long-distance communication optical cable fault automatic detection and positioning system and method, and the method comprises the steps: collecting optical signal data in an optical fiber link in real time through the deployment of a multi-optical fiber sensor or a distributed optical fiber sensing technology; denoising processing is carried out on the collected data, abnormal fluctuation is analyzed by applying a self-adaptive algorithm, and the type of a fault which is about to occur is identified; classifying the faults by using a machine learning classification algorithm; time domain reflection and wavelength multiplexing technologies are combined, a composite algorithm is adopted to accurately position a fault point, and path optimization and error correction are carried out through sensing node data comprehensive analysis; an environment compensation algorithm is introduced, influences of environment factors on the optical signals are analyzed in real time, and errors are eliminated; and based on a fault positioning and analysis result, an AI algorithm is adopted to evaluate a fault influence range, a repair scheme is automatically generated, and priorities are scheduled for repair.

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal data fusion processing method and system, and the method comprises the steps: collecting a multi-modal data set containing the activities of the old through an intelligent old-age care robot; constructing a timestamp calibration matrix based on the multi-modal data set, and carrying out dynamic weight correction on the timestamp calibration matrix by adopting a three-modal data envelope delay compensation algorithm to obtain a three-modal data stream with a synchronous time sequence; performing hierarchical feature extraction through a nested feature extractor to obtain a three-mode tumble feature vector group; performing vector fusion on the three-mode tumble feature vector group to obtain tumble fusion feature vectors; and inputting the tumble fusion feature vector into a multi-modal forward prediction model to perform tumble risk trajectory prediction, and outputting a tumble detection result, the method realizes prediction analysis of body posture changes in a key time window before the old man tumble, and improves the ability to distinguish a slow getting-up action and a real tumble behavior of the old man.

The invention relates to the technical field of aluminum alloy cold stamping quality control, in particular to a multi-source data fusion and real-time optimization-based cold stamping quality control method, which comprises the following steps of: inputting a fusion feature space into a pre-trained LSTM (Long Short Term Memory) model, and associating a real-time stamping speed with a dwell time parameter through an attention mechanism; combined prediction of springback value deviation and defect probability is realized for the first time, a process parameter correction value is dynamically generated based on a prediction result, and a thermal expansion compensation algorithm and micron-sized mold compensation are combined, so that the quality regulation response speed reaches a millisecond level, the compensation precision is improved to + / -2 [mu] m, and the error is reduced by more than 50% compared with that of traditional PID (Proportion Integration Differentiation) control.

The invention discloses a dual-electric brake boosting system for a heavy-duty vehicle and a coordinated control method of the dual-electric brake boosting system, and relates to the technical field of vehicle brake control. The invention discloses a dual-electric brake power-assisted system for a heavy-duty vehicle and a coordinated control method thereof. The coordinated control method comprises the following steps that S1, a multi-source sensor synchronously collects and preprocesses wheel speed, current, suspension and pedal signals; s2, estimating the load in real time through Kalman filtering, and outputting a dynamic load coefficient to guide distribution; s3, triggering interruption when the pedal force change rate exceeds the threshold value, and directly outputting the maximum braking force; s4, dynamically distributing the braking force, predicting the temperature rise, and adjusting the output weights of the double boosters; s5, outputting a digital twinborn verification theory, and starting a compensation algorithm after deviation overrun; s6, encrypting an instruction by a TSN protocol, and ensuring low-delay transmission and communication security; s7, a closed-loop optimization link is formed based on the actual braking data calibration model; the method has the beneficial effect that the real-time performance, safety and reliability of the braking system are comprehensively improved.

The invention relates to a method and system for monitoring the running state of a water pump unit based on the digital twin technology, and belongs to the technical field of pipeline pump monitoring. The method comprises the steps that a digital twin model fusing equipment parameters and environment sensing data is constructed, and multi-dimensional running data such as dynamic pressure, flow, electric energy quality and temperature are collected in real time in combination with the Internet of Things technology; a multi-dimensional correction algorithm is innovatively provided, fluid flow velocity calculation is corrected through pipe opening structure parameters, lift calculation is dynamically corrected through a temperature compensation algorithm and a mechanical wear factor, and dynamic compensation is conducted on the water pump efficiency by introducing a harmonic loss calibration mechanism. Based on a two-channel machine learning model, a fault incidence matrix is constructed by improving a weighted K-means algorithm to realize equipment health state clustering, an efficiency attenuation trend is predicted in combination with an LSTM network, static characteristics and dynamic time sequence data are fused by using an attention mechanism, and finally an accurate fault diagnosis report and a maintenance strategy are generated.

The invention relates to the technical field of gesture recognition, in particular to an anti-interference optimized gesture recognition method. Comprising the following steps: acquiring a gesture video stream through a camera, constructing a dynamic background model by using a frame difference method and a Gaussian mixture model, eliminating a static background and interference, and extracting a target area image; performing local brightness histogram analysis on the target region image, and optimizing the image quality by adopting a region adaptive compensation algorithm and a multi-scale edge enhancement technology; positioning a gesture area in real time by using a color histogram and a feature matching algorithm, and dynamically updating a gesture track in combination with Kalman filtering; and extracting gesture shapes, tracks and dynamic mode features through deep learning, comparing the features with a standard model library, and outputting gesture categories and corresponding function instructions. According to the method, a multi-level optimization strategy is adopted for a complex background, a dynamic target and a changeable illumination environment, so that the anti-interference capability and the recognition precision of gesture recognition are improved.

The invention relates to an intelligent self-adaptive gas sensor adjustment controller and a working method thereof, and belongs to the technical field of gas detection. The adjustment controller comprises a main control module, a gas detection module, an environmental parameter module, an adjustment execution module, a communication module and a power management module, the main control module compensates detection parameters obtained by the gas detection module according to environmental parameter information obtained by the environmental parameter module in combination with built-in standard parameters and a compensation algorithm, and the adjustment execution module performs adjustment according to the compensation parameters. And whether the calibration execution module is started to calibrate the gas detection module is determined by judging the output stability of the gas detection module. According to the control method of the adjustment controller, through intelligent and self-adaptive adjustment control, the measurement precision, stability and applicability of the gas sensor are remarkably improved, and a reliable solution is provided for gas detection in a complex environment.

The invention relates to a concrete temperature-stress two-parameter cooperative monitoring and early warning method and system, and belongs to the technical field of concrete structure health monitoring, and the method comprises the steps: activating a composite sensor network disposed at a preset position of a concrete structure, and executing a sensor self-calibration mode; the method comprises the following steps: synchronously acquiring temperature and stress original monitoring data of each node, processing based on an altitude air pressure compensation algorithm, and outputting a temperature gradient field matrix and a stress tensor sequence with aligned time domains; the method comprises the following steps: separately calculating a thermal stress component and an effective stress component, performing integral calculation on hydration reactivity, generating a multi-dimensional feature vector set, inputting a pre-constructed prediction model, executing time sequence evolution prediction, calculating a crack probability value, performing environment correction in combination with real-time environment parameters, outputting a risk level identifier, and matching a preset regulation and control strategy. And generating an equipment control instruction set and executing corresponding regulation and control actions. According to the invention, the accuracy and timeliness of crack risk prediction can be improved.

The invention discloses a cloud mobile phone end-to-end performance tracking method and related equipment, and relates to the technical field of cloud computing, and the method comprises the steps: obtaining client touch event data and network event data, and generating a global unique identifier based on a preset Hash algorithm; obtaining server resource event data; performing timestamp calibration on the client touch event data and the server resource event data based on a hardware-level clock synchronization and software compensation algorithm to generate a synchronous timestamp; based on the synchronization timestamp, aligning the event sequences of the client and the server through a dynamic time warping algorithm to generate an aligned event sequence; performing causal probability calculation on the aligned event sequence based on a Bayesian network model, and determining a causal relationship weight between resource events; and performing root cause matching according to the causal relationship weight and a preset abnormal mode library, generating a root cause list with priority ranking, and triggering execution of a self-healing strategy.

The invention discloses a power equipment defect detection method and system, and belongs to the technical field of image detection, and the method comprises the steps: carrying out the multi-angle image collection of the surface of a transformer through a camera device, and carrying out the image preprocessing through a self-adaptive illumination compensation algorithm; a Faster R-CNN model is utilized to carry out defect area detection on the preprocessed image, and positioning of a defect area is optimized through a non-maximum suppression algorithm; based on the optimized defect area, defect features are extracted by using a multi-modal feature extraction network, feature vectors are generated, and defect classification is carried out; performing correlation analysis on the historical data and the defect classification result through a graph neural network, judging a defect reason and generating a defect detection report, collecting a field maintenance result and updating a defect sample library; and automatic detection and classification of the defects of the power equipment are realized.

The invention relates to the technical field of production line control, and discloses an intelligent control method and system for production line mold machining. According to the method, a pressure distribution state is determined by obtaining initial cutting parameters of a cutter and surface pressure data of a mold, mold node stress distribution is calculated by utilizing a three-dimensional finite element model, and a stress concentration position is identified. And predicting a mold deformation trend through a linear regression model according to the thermal expansion coefficient and the environment temperature data. Meanwhile, the initial cutting parameters are optimized by combining a cutting parameter optimization objective function, and the objective function comprehensively considers factors of tool wear, surface quality and residual stress. And on the basis of a die deformation prediction result, a dynamic compensation algorithm is adopted to adjust supporting structure parameters and optimize cutting parameters, and finally optimal cutting parameters and a target tool path are generated. According to the method, precise coordination control over pressure distribution and mold deformation under the complex working condition is achieved, the residual deformation risk is reduced, and the mold machining precision is improved.

The invention discloses a method for improving transformer area crosstalk of HPLC (High Performance Liquid Chromatography) and micropower wireless dual-mode communication, which comprises the following steps of: S1, electrifying an acquisition terminal equipment node, and aligning signal acquisition time windows of two communication modes; s2, combining the obtained dual-mode signal parameters with a preset transformer area topology database to generate feature vectors of multi-dimensional features; s3, training to generate a random forest model, inputting feature vectors of multi-dimensional features, dynamically calculating the affiliation weight value of the transformer area, and carrying out nonlinear weighted calculation on the feature vectors; and S4, on the basis of weight comparison and transformer area boundary node processing, correcting measurement errors by combining the transformer area attribution weight value with a preset impedance shielding compensation algorithm, generating a unique transformer area identifier, locking a networking relationship, and executing transformer area attribution judgment. According to the method, the wide coverage advantage of HPLC is reserved, meanwhile, the space attenuation characteristic of wireless communication is used for achieving accurate recognition of the transformer area boundary, and the installation and acquisition stability of the use and acquisition terminal equipment can be improved.

The invention relates to the technical field of forklift driving stability control, in particular to a forklift driving stability control system based on ultrasonic and laser composite ranging, which comprises a composite ranging module, a multi-modal data fusion module, a stability prediction and control module, an execution mechanism control module and an online learning system. The composite distance measuring module collects surrounding environment information and corrects motion errors through a dynamic compensation algorithm; the multi-modal data fusion module dynamically adjusts the weight of the sensor based on an environmental interference factor, and realizes space-time calibration in combination with extended Kalman filtering; the stability prediction and control module is fused with the vehicle dynamic characteristics to predict the rollover risk and generate a hierarchical control instruction; the actuating mechanism control module drives actuating components such as an active suspension and an electronic differential mechanism through a layered framework. The method can adapt to complex working conditions, the prediction precision and fault-tolerant capability are improved, safety and efficiency are balanced, the maintenance cost is reduced, and the method is suitable for various forklifts.

The invention discloses a wind profile radar radial speed quality control and horizontal wind field inversion method. According to the method, through the steps of multi-mode detection splicing, signal-to-noise ratio threshold value quality control, beam consistency inspection, horizontal wind component inversion, space and time continuity inspection and the like, the quality control process of the wind field data is optimized, the precision of the horizontal wind field data is remarkably improved, and particularly, the root-mean-square error and deviation in high-level data are remarkably reduced. The core innovation comprises a mode splicing strategy based on sounding data comparison, dynamic signal-to-noise ratio threshold calculation, threshold setting of beam consistency check and a wind component compensation algorithm when a vertical beam is missing. Through experimental verification, compared with a traditional wind profile radar data processing method, the wind profile radar data processing method has remarkable advantages in data accuracy and stability.

The invention relates to a quick alignment method for optical coupling equipment, which comprises the following steps of: acquiring real-time position information of a laser transmitting end and a laser receiving end, acquiring relative position data by adopting a fiber grating technology, setting a dynamic compensation algorithm according to a light spot offset and a position deviation value, generating an adjustment parameter through the dynamic compensation algorithm, and aligning the adjustment parameter to the optical coupling equipment. According to the method, the structural design of optical coupling equipment is optimized, a high-precision light spot imaging module and a fiber grating positioning module are integrated, and a multi-point data fusion technology and an optical path alignment model in a dynamic environment are utilized, so that the measurement precision is further improved, and the influence of environmental interference is reduced. According to the invention, the problems of real-time performance and accuracy of light path alignment in kilometer-level laser measurement are effectively solved, and a new solution is provided for the development of a long-distance laser measurement technology.

The invention discloses a high-precision linear vibration feedback motor system, and relates to the technical field of precise electromagnetic driving, and the system comprises a core driving module which is a moving magnet type linear motor of a Halbach array magnetic circuit structure, and is integrated with a multi-physics field sensing unit; the dynamic parameter tracking module is used for acquiring a reed rigidity coefficient, a damping coefficient and a magnetic constant in real time when the motor runs, and establishing a dynamic displacement model based on a recursive least square method; a double-closed-loop control framework is adopted, an inner loop adopts field-oriented control, and an outer loop is based on model prediction control; the aging prediction unit activates a temperature rise compensation algorithm when the accumulated number of vibration times is greater than a set threshold value; the self-adaptive calibration engine is used for injecting a sweep frequency excitation signal when the system is started, and automatically compensating individual difference parameters according to the harmonic peak offset; and the digital twin mapping unit is used for establishing a real-time mapping relationship between the physical parameters of the motor and the virtual model, and dynamically correcting the model parameters by comparing the actual displacement with the model displacement.

The invention relates to the field of industrial automation, and particularly provides a semiconductor photoetching system based on an intelligent industrial robot. And a multi-sensor fusion and real-time compensation algorithm is adopted, so that photoetching positioning errors are accurately sensed and corrected, the positioning precision is greatly improved, and the stable performance of the chip is ensured. According to a multi-physical field monitoring and self-adaptive control algorithm, process parameters are dynamically regulated and controlled according to a photoetching environment and a photoresist state, pattern transfer is optimized, pattern defects are avoided, and photoetching pattern quality is improved. According to the multi-modal data fusion and deep learning algorithm, photoresist coating parameters are automatically adjusted according to the surface characteristics of the wafer, uniform coating is realized, and the stability of the photoetching process is enhanced. The reinforcement learning algorithm assists the multiple robots in efficient collaboration, optimizes task allocation, improves the overall photoetching efficiency and the resource utilization rate, and promotes the progress of the semiconductor photoetching technology in all directions.

The invention discloses a logistics carbon emission data monitoring system based on a block chain and edge calculation, and relates to the technical field of logistics systems. According to the logistics carbon emission data monitoring system based on the block chain and edge calculation, on the aspect of data integration and estimation, multi-source heterogeneous data is fused to construct a dynamic compensation model, a lag compensation algorithm is developed, emission factors are adjusted, more accurate real-time carbon emission estimation is achieved, and full-chain data standardization and association are achieved through middleware; in the aspect of privacy protection, the blockchain and zero-knowledge proof technology are combined, enterprise data authenticity verification is ensured, original information is not leaked, a carbon emission result is subjected to chaining evidence storage, original data is encrypted and stored, and cross-enterprise federal learning ensures that data cannot be out of a domain; the logistics carbon emission data monitoring accuracy, privacy protection and auditing supervision intelligent level are improved.

The invention relates to a 3D printing method and system for a composite prestressed reinforcing rib, and solves the main problems of weak interlayer adhesive force, large thermal stress and lack of an effective reinforcing rib adding method in the mechanical property of an existing 3D printing technology. Printing parameters and prestress release are cooperatively controlled on the basis of characteristic parameters of printing materials through a stress linkage algorithm, vertical ribs are tensioned for the second time through an interlayer compensation algorithm every time a preset number of layers are printed, and the printing parameters are dynamically adjusted through a path optimization algorithm in a high-stress area; when printing is completed and the material is not completely cured, stress distribution of all the reinforcing ribs is scanned through a stress balance algorithm and adjusted to a preset deviation range, and it is ensured that stress distribution of the whole structure is uniform; and after the printing material and the reinforcing ribs are compounded and cured, corresponding curing is performed according to the characteristics of the printing material. The 3D printing structure has the following effects that the mechanical property and reliability of the 3D printing structure are improved, and the interlayer strength and the overall stress distribution are optimized.

The invention discloses a force control joint torque self-correction method based on digital twinborn mapping, which comprises the following steps of: acquiring and preprocessing multi-source operation data of a force control joint to generate a standardized input data set; establishing a three-layer digital twinning mapping model to realize physical, simulation and cognitive layer mapping; a bias load device is arranged, and angle, torque, current and temperature signals are collected; space-time tensor compensation is executed on the simulated twinborn layer, and a dynamic compensation value is generated; bootstrap consistency correction is executed on the cognitive twin layer, and comprehensive correction parameters are fused; and inputting comprehensive parameters to adjust a simulation layer structure, outputting a correction torque and performing closed-loop updating. By constructing the three-layer digital twinning mapping model and introducing the torque compensation algorithm, torque dynamic error identification, adaptive compensation and real-time self-correction of the force control joint under complex working conditions are realized, and the measurement precision and the system stability are improved.

The invention discloses a ground wire multi-machine synchronous traction pay-off scheduling method and system, relates to the technical field of high-voltage transmission line construction, and is used for solving the problem of insufficient multi-machine real-time tension synchronization precision in the existing method. A tension sensor and a speed sensor which are arranged on each traction device collect a tension value, a traction speed value and position coordinate data of a ground wire in real time, and transmit the data to a central scheduling controller; establishing a multi-machine unified time synchronization reference and tension reference model, calibrating a timestamp through a GPS module, and initializing a threshold value based on historical parameters; comparing tension values one by one to calculate a deviation vector sequence; activating a compensation algorithm to execute a speed fine adjustment and tension compensation instruction; iteratively calculating the feedback adjustment value until the deviation is stable; and integrating the synchronous data to the overall paying-off scheduling model to dynamically update the path planning and speed curve. According to the method and the system, the problem of insufficient multi-machine real-time tension synchronization precision in the prior art is effectively solved.

The invention provides a self-adaptive adjustment control system and method applied to natural gas flow calibration, and relates to the technical field of automatic control and adjustment systems.The method comprises the steps that real-time pipeline operation data are collected, and real-time flow state recognition is conducted to obtain real-time flow state data of a preset type; the instruction generation module is used for determining a trigger coefficient, judging whether a trigger condition is met or not, and generating a calibration trigger instruction if the trigger condition is met; the data optimization module is used for receiving a calibration trigger instruction, acquiring a current compensation parameter, generating a preliminary correction coefficient, calling reference data and determining a final optimization correction parameter set in combination with the calibration data packet; the calibration updating module is used for updating the real-time compensation algorithm, determining the deviation degree, and re-triggering the calibration process if the deviation degree exceeds a preset allowable range; and the process optimization module is used for extracting current key calibration data, calling historical calibration data, determining an optimal calibration period and optimizing the calibration process based on the optimal calibration period after the calibration process is finished. And the calibration precision and efficiency of the flowmeter are improved.

The invention relates to a course axis drift suppression method and device, a holder and a medium, and the method comprises the steps: receiving original data of a current course, the original data comprising an absolute angle outputted by a course encoder, and an acceleration and an angular velocity outputted by an IMU module; the original data are converted into a unified coordinate system through a rotation matrix, and mechanical installation deviation is compensated; performing data fusion on the compensated original data through a preset fusion algorithm; and outputting the fused absolute angle to a servo mechanism of the holder, and by adopting the method, through real-time sensor data alignment, a dynamic compensation algorithm and a fusion algorithm, a course drift suppression mechanism which is resistant to dynamic interference and low in cost is constructed, and static drift lt can be realized; the dynamic tracking error is 1t, 0.01 degree / h and 1t; and the technical index is 0.05 degree.

The invention relates to the technical field of building surveying and mapping, and provides an old building digital surveying and mapping system and method based on air-ground collaborative point cloud fusion, and the method comprises the steps: carrying out the blind area detection of space-time fusion data scanned by unmanned aerial vehicle equipment and a ground robot, and carrying out the blind area detection when the top point cloud data / bottom point cloud data have a blind area visual angle; starting a dynamic master-slave mechanism; according to the dynamic master-slave mechanism, equipment corresponding to the point cloud data with the blind area serves as slave equipment, equipment corresponding to the point cloud data without the blind area serves as host equipment, the slave equipment is controlled to execute point cloud filling, blind area compensation data are determined, and after the blind area compensation data are fused into time-space fusion data, the time-space fusion data are subjected to time-space fusion. And generating target space-time fusion data loaded to the visual interface. A closed-loop workflow from data acquisition to quality verification is constructed through a dynamic master-slave control mechanism, a multi-dimensional synchronization strategy and an intelligent compensation algorithm driven by architectural features.

The invention discloses a regional fan virtual inertia support scheduling method considering a wind power plant group synergistic effect, and aims to solve the problems of non-uniform inertia distribution, response conflict and economy in wind power plant group scheduling. By introducing a hybrid multi-level cooperative control architecture, the inertia vacancy of the power grid is calculated in real time, the inertia demand of each wind power plant is allocated, and the scheduling strategy of the wind power plant group is optimized. And a time-varying inertia demand curve is generated by adopting a hybrid algorithm, the future inertia demand is accurately predicted, and the fan rotating speed recovery cost, the equipment loss and the wind curtailment amount are minimized in combination with a multi-target optimization model. A model predictive control framework and a Nash equilibrium theory are introduced, the game problem between wind power plants is solved, collaborative scheduling of a wind power plant group is optimized, and timeliness and stability of a scheduling instruction are ensured by embedding a time delay compensation algorithm. According to the method, the inertia supporting efficiency of the power grid can be remarkably improved, the equipment loss and the wind curtailment amount are reduced, the robustness of the system is improved, and the method has high application value and prospect.

The invention relates to the technical field of chip test engineering control, in particular to an active temperature control method and system for a chip aging tester, and the method comprises the steps: analyzing a test process according to test task parameters, and evaluating the expected thermal load change rate of a test chip, so as to dynamically generate a chip target junction temperature control curve; based on the chip target junction temperature control curve, the actual chip junction temperature and the current actual furnace temperature, the theoretical furnace temperature is obtained through a junction temperature closed-loop control algorithm, the furnace temperature compensation amount is calculated through a feedforward compensation algorithm in combination with the test state parameters, and the ideal furnace temperature is obtained according to the theoretical furnace temperature and the furnace temperature compensation amount; the actual temperature control quantity is obtained according to the basic temperature adjusting quantity and the temperature control compensation quantity, a preset chip target junction temperature control curve can be tracked more accurately, transient and steady state errors caused by test program switching and environment fluctuation can be more effectively restrained, and control over the chip junction temperature with higher precision and faster response is achieved.