1358 results about "Kalman filter algorithm" patented technology

The invention relates to an autonomous navigation method for lunar satellite formation and a second-order filtering navigation architecture, and the autonomous navigation method comprises the steps: obtaining orbit prediction information based on a kinetic model of the lunar satellite formation, and obtaining collaborative theory observation information based on a collaborative observation model, carrying out fusion processing on orbit prediction information, collaborative theory observation information and multi-source observation data of in-orbit observation by using an extended Kalman filtering algorithm, and estimating an absolute orbit state of the lunar satellite formation; establishing a relative motion model of the slave satellite under the local coordinate system of the master satellite, and establishing a relative measurement model; and taking the absolute orbit state of the master satellite as a reference datum, obtaining a preliminary prediction relative orbit state based on the relative motion model of the slave satellite, and obtaining relative measurement data including distance measurement and angle measurement based on the relative measurement model, and fusing the relative measurement data and the preliminarily predicted relative orbit state through an extended Kalman filtering algorithm to estimate the relative orbit state of the slave satellite. Navigation may be independent of ground support.

The invention discloses a ship-shore cooperative tracking and positioning method based on multi-modal sensor fusion, and belongs to the technical field of target positioning. The method comprises the steps that multi-sensor layout and sensor fusion calibration are carried out on a target ship set and a shore end respectively, multi-view image sequence data and three-dimensional point cloud data are obtained, and the target ship set comprises a plurality of target ships; performing data fusion based on the multi-view image sequence data and the three-dimensional point cloud data to obtain fusion data of the target ship set, establishing an adaptive motion state model and an adaptive observation model based on the fusion data, and performing state prediction, state updating, data association and tracking management on the target ship set by adopting an unscented Kalman filtering algorithm; and establishing a space-time diagram model based on the Kalman filtering fusion observation factor and the Kalman filtering state prediction factor, and performing pose optimization on the target ship set in combination with the GPS factor. According to the method, the accuracy of ship and ship-shore cooperative positioning is improved.

The invention provides a multi-physics field cooperative regulation and control method in coal mine rock burst prevention and control, and relates to the technical field of coal mine rock burst prevention and control, and the method comprises the steps: synchronously collecting stress field, fracture field and vibration wave field data in real time through a distributed optical fiber sensor network and a micro-seismic monitoring system, and constructing a dynamically updated three-dimensional geomechanical model; based on an adaptive Kalman filtering algorithm, noise reduction multi-source data are fused, a dynamic stress concentration factor and an energy accumulation critical index are extracted, and a rock burst risk probability model is established; a multi-objective optimization algorithm is adopted to generate graded regulation and control instructions of grouting reinforcement, mining speed adjustment and pressure relief drilling, and the graded regulation and control instructions are executed in real time; and iteratively optimizing the model weight by using a transfer learning algorithm in combination with a historical case library to form a closed-loop feedback adaptive prevention and control system. According to the method, the analysis precision of rock mass fracture evolution under complex geological conditions is improved through multi-physics field collaborative perception and dynamic modeling, and risk quantification and real-time regulation and control are realized through multi-algorithm coupling analysis.

The invention provides a breakwater monitoring data preprocessing method and system based on Kalman filtering, and relates to the technical field of breakwater structure safety monitoring. The method comprises the following steps: acquiring original motion data of acceleration, inclination and displacement through a motion attitude sensor to obtain an original data sequence; initializing a state vector and an error covariance matrix; dynamically correcting the state transition matrix and calculating a prediction state vector and a prediction error covariance matrix; a Kalman gain is generated; updating a state vector and an error covariance matrix; and extracting the filtered motion data as a preprocessing result. According to the method, the state transition matrix is dynamically corrected by introducing the wave force feedback, so that the Kalman filtering algorithm can adapt to the wave impact environment, noise interference in monitoring data is effectively inhibited, and the accuracy and reliability of key motion parameter data of the breakwater are remarkably improved.

The invention discloses an unmanned aerial vehicle cluster collaborative target hunting method based on brain-like calculation, and belongs to the technical field of autonomous navigation robot and multi-robot coordination control, and the method comprises the steps: obtaining a multi-view image under a camera view, and obtaining the local pose and covariance of each unmanned aerial vehicle; obtaining a global consistent relative pose of the unmanned aerial vehicle cluster according to the local pose and the covariance of each unmanned aerial vehicle; performing state estimation on the target by adopting an unscented Kalman filtering algorithm to obtain target state information under a unified coordinate system; using a Bezier curve generation algorithm to obtain a target motion trail in a section of historical state; the method comprises the following steps: expanding an unmanned aerial vehicle cluster through a centroid extension method to obtain a surrounding queue of target surrounding, solving the minimum surrounding cost by adopting a Gaussian Newton method, and generating a surrounding position of each unmanned aerial vehicle; the motion trajectory of the unmanned aerial vehicle is generated by adopting mixed A * search, and a final surrounding trajectory sequence is generated, so that the accuracy of motion state estimation is remarkably improved.

The invention relates to a dynamic power regulation and control method and system for an electric vehicle charging pile, and the method comprises the following steps: S1, based on the real-time operation data of a charging pile cluster, employing a multi-source information fusion technology, collecting a power grid voltage fluctuation signal through a Hall sensor disposed at a charging pile end, and combining with the battery charge state data transmitted by a vehicle-mounted BMS, and eliminating noise interference by using an improved Kalman filtering algorithm, and realizing multi-dimensional data fusion by using a D-S evidence theory to generate a multi-source working condition feature set. The method has the advantages that the power grid voltage fluctuation signal and the battery charge state data are integrated through the multi-source information fusion technology, the improved Kalman filtering algorithm is adopted to eliminate noise interference, multi-dimensional data fusion is achieved in combination with the D-S evidence theory, the integrity and reliability of working condition feature extraction are remarkably improved, and the working condition feature extraction efficiency is improved. And the LSTM-ARIMA hybrid prediction model is utilized to synchronously process the nonlinear features and the periodic rules.

The invention belongs to the technical field of ocean detection, belongs to a multi-sensor fusion intelligent anti-collision method and system, comprises a sensing layer, a processing layer and an application layer, and provides an intelligent anti-collision and evidence recording ocean monitoring floating system integrating computer vision, target ranging, satellite positioning and ship automatic recognition system multi-sensor fusion. According to the invention, YOLOv8 target detection, Transform data fusion and a Kalman filtering algorithm are adopted, so that accurate detection and anti-collision early warning of ships and floating objects on the sea are realized. The system has an AIS failure processing mechanism and an evidence encryption storage function, and ensures reliable operation and data compliance under complex sea conditions.

The invention provides a photovoltaic power station energy storage management system and method, and relates to the technical field of power system energy storage. The photovoltaic power station energy storage management system comprises the following modules: a data acquisition module, a prediction analysis module, an optimization scheduling module, a health management module, a real-time control module, a power grid interaction module and an energy efficiency evaluation module. And the data acquisition module is used for cleaning photovoltaic array output voltage / current data by adopting an improved Kalman filtering algorithm based on an Internet of Things sensing network, integrating environmental parameters such as irradiance and temperature of a meteorological station through a multi-source data fusion technology, and generating a standardized operation data set. According to the method, photovoltaic array output data is cleaned and integrated through an improved Kalman filtering algorithm and a multi-source data fusion technology, a high-precision standardized data set is constructed, a dual prediction architecture of an attention mechanism long-short term memory network and a time convolution network is combined, and the space-time correlation between photovoltaic output and load demand prediction is remarkably improved.

The invention relates to the technical field of photovoltaic inspection, in particular to an unmanned aerial vehicle photovoltaic inspection positioning method and system based on extended Kalman filtering, and the method comprises the steps: obtaining the GPS positioning data, attitude data and photovoltaic panel image data of an unmanned aerial vehicle in real time based on a sensor carried by the unmanned aerial vehicle; according to the image data of the photovoltaic panel, fault features of the fault photovoltaic panel are identified, and pixel coordinates of the fault photovoltaic panel are extracted; converting the pixel coordinates of the fault photovoltaic panel into initial position information under a geographic coordinate system; constructing an extended Kalman filtering model, inputting the positioning data, attitude data and initial position information of the unmanned aerial vehicle into the extended Kalman filtering model, and establishing a state vector and an observation vector; and iteratively calculating an optimal estimation value through an extended Kalman filtering algorithm according to the state vector, and outputting geographic coordinates of the fault photovoltaic panel. According to the invention, the method achieves the quick and precise positioning of the fault photovoltaic panel, improves the operation and maintenance efficiency of a photovoltaic power station, and reduces the operation and maintenance cost.

Disclosed are an inertial baseline integrated navigation method, and a product, a medium, a device and an autonomous underwater vehicle, relating to the field of navigation. The method comprises: in an initial stage of the starting of an inertial baseline integrated navigation system, an SINS using a USBL to assist with initial alignment and obtaining initial attitude information (1); when the inertial baseline integrated navigation system enters a formal navigation operating state, the SINS performing pure inertial navigation solving on the basis of the initial attitude information, so as to obtain navigation parameter calculation values including errors (2), wherein the navigation parameter calculation values comprise the attitude, velocity and position; on the basis of the navigation parameter calculation values including the errors, constructing a navigation state error model based on a Lie group (3); on the basis of a Huber function, constructing a generalized maximum likelihood estimation model (4); according to the navigation state error model based on a Lie group and the generalized maximum likelihood estimation model, establishing an integrated navigation robust Kalman filtering algorithm (5); on the basis of the integrated navigation robust Kalman filtering algorithm, performing filtering estimation on the inertial baseline integrated navigation system, so as to obtain a state estimation value (6); and on the basis of the state estimation value, performing feedback updating on navigation parameters, and on the basis of the updated navigation parameters, performing navigation. The method can improve the integrated filtering precision and positioning and orientation precision.

The invention relates to an unmanned aerial vehicle self-adaptive wind-resistant flight control method and system based on multi-mode environment perception. The method comprises the steps that a miniature wind speed and direction sensor array obtains real-time wind speed and wind direction information of the surrounding environment of an unmanned aerial vehicle; the inertial measurement unit obtains real-time attitude angle, angular velocity and acceleration information of the unmanned aerial vehicle; the visual flow sensor obtains real-time image data of the surrounding environment of the unmanned aerial vehicle; the data fusion and wind field estimation module receives multi-source data collected by the miniature wind speed and direction sensor array, the inertial measurement unit and the visual flow sensor, carries out fusion processing on the multi-source data based on an extended Kalman filtering algorithm, and predicts to obtain predicted wind field information in a future short time window; the flight control core module adopts an adaptive sliding mode control algorithm to calculate a compensation control quantity for counteracting wind disturbance, and performs real-time control on the unmanned aerial vehicle based on the compensation control quantity; and the wind resistance and the operation reliability of the unmanned aerial vehicle in a complex dynamic environment are improved.

The invention provides a medical RFQ accelerator low-level signal phase stability control method and system. The method comprises the following steps: constructing a time-frequency energy spectrum feature vector based on wavelet packet transformation; extracting a second disturbance feature based on a lightweight convolutional neural network and an attention mechanism; constructing a phase dynamic trend prediction module based on a long short-term memory network, and obtaining a first prediction phase error; constructing a phase compensation module based on a residual control network to obtain a second phase compensation amount; and outputting a real-time driving control signal based on the extended Kalman filter. According to the method, the time-frequency energy spectrum feature vector based on wavelet packet transformation is constructed, accurate characterization of the multi-scale disturbance features of the low-level signals is achieved, a medical RFQ accelerator phase dynamic compensation system is established in combination with a deep learning network and an extended Kalman filtering algorithm, the control precision and the anti-interference capability of signal phase stability are remarkably improved, and the method is suitable for popularization and application. The method is suitable for a high-precision medical particle accelerator control system.

The invention discloses a multi-base-station AOA cooperative low-altitude target rapid positioning system. The system comprises an AOA measurement base station network, an AOA data preprocessing module, an adaptive weighted intersection positioning module, an extended Kalman filtering state estimation module and a data fusion and system integration module. The system synchronously measures the arrival angle of a target signal through multiple base stations, removes noise in combination with smoothing filtering and an anomaly rejection algorithm, and then solves the initial position of a target by using a self-adaptive weighted intersection algorithm based on measurement quality and geometric distribution. And then, fusing the target motion model and the measurement model by adopting an extended Kalman filtering algorithm to realize dynamic estimation and prediction of the position, the speed and the course. The system can realize high-precision and real-time positioning and continuous tracking of targets such as low-altitude unmanned aerial vehicles, small aircrafts and the like in a complex electromagnetic environment and a sight distance limited scene, and has visual display and regional alarm functions.

The invention relates to the technical field of pipeline construction, and discloses a water supply pipeline installation auxiliary method and system based on multi-source positioning and digital twinning, and the method comprises the steps: constructing a digital twinning model based on a three-dimensional point cloud modeling technology, and integrating the geographic information of a construction area, high-voltage line corridor parameters and pipeline installation path planning data; a multi-source positioning sensor group is arranged on hoisting equipment and a pipeline, and spatial position, attitude and environmental electric field data are collected in real time and transmitted to a data fusion processing module; multi-source data are fused through a Kalman filtering algorithm, and high-precision positioning is realized in combination with a digital twinborn model; dynamically adjusting a positioning mode based on an adaptive switching mechanism; construction parameters are generated in real time, dynamic monitoring and early warning are conducted, and when a safety threshold value is exceeded, alarm is triggered and operation is suspended. The system corresponds to the method. By the adoption of the method, the positioning precision and environmental adaptability of water supply pipeline installation are improved, the construction safety risk of complex scenes is reduced, and the construction efficiency is improved.

The invention belongs to the technical field of pipe network monitoring control, and particularly relates to an urban pipe network leakage detection method and system.The urban pipe network leakage detection method includes the steps that firstly, data are collected at key nodes and pipe sections of a pipe network through a data collection module, and a four-dimensional original data set with spatial positioning attributes is formed; the preprocessing module adopts an improved Kalman filtering algorithm containing a pipe network material attenuation coefficient to reduce noise, and outputs high-quality data; the space-time fusion module is combined with GIS pipe network topological data, the topological weight is calculated through a node degree centrality algorithm, and a time attenuation mechanism and adjacent node features are fused to generate a comprehensive feature matrix; the dynamic judgment module judges suspected leakage based on a multi-parameter weighting model and a sliding window dynamic threshold value; the positioning engine module positions a leakage point through a two-factor algorithm of signal time difference and improved hydraulic model residual error; and finally, the linkage control module starts secondary acoustic verification, valve control, early warning and maintenance scheduling instructions are automatically generated after confirmation, and closed-loop management from detection to disposal is achieved.

The invention provides a multi-source data fusion self-positioning method and system. The method comprises the following steps: acquiring GPS positioning data, visual image data and laser radar point cloud data of an unmanned aerial vehicle; carrying out noise reduction preprocessing on the GPS positioning data by adopting an unscented Kalman filtering algorithm; performing sensor joint calibration based on a visual image and a laser radar point cloud, and establishing a geometric mapping relation between a camera coordinate system and a world coordinate system by retrieving a preset high-precision tower ledger library and solving a PnP problem; inputting the image target detection data, the GPS state estimation value and the geometric mapping data into a pre-trained auto-encoder regression network; anti-interference potential features are extracted through an encoder of the network, and a target position estimation value of the target space position of the unmanned aerial vehicle is output through a regression head. According to the invention, the problem of positioning drift caused by strong electromagnetic interference and complex landform in electric power inspection is effectively solved.

A low-delay multi-protocol intelligent lighting synchronization system based on a unified abstraction layer comprises the steps that an instruction from a multi-protocol bridging module is received, and network round-trip time data of terminal equipment is acquired through an active detection mechanism; based on the network round-trip time data, adopting a Kalman filtering algorithm to construct a delay prediction model so as to predict the future network delay of the terminal equipment; inputting the instruction into a third-stage buffer area for processing, and dynamically adjusting the trigger frequency of the active detection mechanism according to the load state of the third-stage annular buffer area; calculating queue delay of the buffer area according to the received load state of the buffer area, and obtaining final sending time of the instruction in combination with future network delay of the terminal equipment; and at the final sending time, sending the instruction from the sending buffer area to the corresponding terminal equipment. According to the system, low-delay and high-stability synchronization of cross-protocol equipment in the field of intelligent illumination is realized.

The invention discloses a lithium battery health assessment method based on an adaptive extended particle filter algorithm. The method comprises the following steps: constructing a second-order RC equivalent circuit model of a lithium battery; determining all parameters in the lithium battery second-order RC equivalent circuit model by using a recursive least square algorithm with a forgetting factor to obtain a determined lithium battery second-order RC equivalent circuit; estimating SOC parameters of the lithium battery at the moment by combining a particle filtering algorithm with an adaptive extended Kalman filtering algorithm; correcting the terminal voltage of the second-order RC equivalent circuit of the lithium battery at the moment to obtain the terminal voltage of the equivalent circuit; and based on the lithium battery second-order RC equivalent circuit and the corrected terminal voltage, estimating the SOE parameter of the lithium battery at the moment k by using a particle filtering algorithm in combination with an extended Kalman filtering algorithm. The SOC and the SOE of the lithium battery are accurately estimated, and the sampling efficiency and the estimation stability of particle filtering are improved. Cooperative joint estimation of SOC and SOE complements each other, and the accuracy of overall state estimation is further improved.

The embodiment of the invention discloses a digital key ranging value filtering method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring ranging information of a plurality of UWB anchor points in real time; obtaining the position change trend of the digital key according to the distance measurement information; under the condition that the effective distance measurement value does not exist, a preset target position is obtained according to the positioning result corresponding to the moment t of the previous effective distance measurement value, the position change trend and the unlocking and locking state of the vehicle terminal, and a predicted distance is obtained according to the distance relation between the effective distance measurement value at the moment t and the preset target position; obtaining a prediction duration T according to the prediction distance; in the T, when an effective distance measurement value is not detected, state one-step prediction of a Kalman filtering algorithm is executed to obtain a filtered distance measurement value, and when the effective distance measurement value is detected, the state one-step prediction is terminated, and a filtering estimation value is obtained according to the Kalman filtering algorithm to serve as the filtered distance measurement value; and obtaining a positioning result of the area outside the vehicle according to the filtered distance measurement values of the plurality of anchor points.

This invention discloses a monocular ranging system and method for underground unmanned vehicles, comprising the following steps: During the vehicle's travel route, images of the preceding vehicle are acquired via sensors, and detection box information and the type information of the preceding vehicle are automatically identified; the vehicle pose of the preceding vehicle is determined based on the type information and detection box information; the pixel coordinates of the closest point on the preceding vehicle to the sensor are obtained in the preceding vehicle image based on the vehicle pose; the measured distance between the closest point and the sensor is obtained, and the measured distance is then predicted and updated to obtain the vehicle distance. By real-time perception of the vehicle body, rear, and wheel hubs, a multi-target information fusion method is proposed based on the detection results, providing a prerequisite for accurate ranging. Then, a two-stage accurate ranging method is adopted, combining a traditional geometric ranging model with a Kalman filter algorithm to further improve the accuracy of the final result, thereby improving the stability and robustness of the ranging of the underground unmanned vehicle.

The invention relates to an automatic guided vehicle (AGV) adaptive path tracking method based on MPC. The method comprises the following steps: S1, establishing a kinematic discretization error model based on the kinematic characteristics of the two-wheel differential AGV, processing a continuous kinematic equation by adopting an Euler discretization method, expressing a dynamic change relationship between a transverse deviation distance and an angle deviation in a state-space equation form, and generating a kinematic discrete state-space model; the method has the advantages that the continuous equation is processed by establishing the kinematics discretization error model and adopting the Euler discretization method, the deviation dynamic relation is expressed through the state space, the calculation process is simplified, the precision is kept, sensor data are fused, the wheel type odometer, IMU and laser data are integrated through the extended Kalman filtering algorithm, and the precision is improved. The real-time position and angle deviation are calculated, the positioning accuracy is improved, a model prediction controller objective function is designed, a weight matrix and boundary constraint are introduced according to constraint conditions, and the effect of obstacle avoidance constraint is combined.

The invention discloses a dual-band InSar and GNSS fused high-precision three-dimensional deformation adaptive monitoring method, and the method comprises the following steps: constructing a dual-band observation equation, and decomposing the dual-band observation equation to obtain a dual-band InSAR resolving residual error; calculating the comprehensive weight of each GNSS observation point through an entropy weight method, selecting a plurality of GNSS observation points with high comprehensive weights as an optimal GNSS reference point combination, and converting the three-dimensional deformation observed by the optimal GNSS reference points into an LOS direction; expanding a two-dimensional radial basis function to a three-dimensional radial basis function, optimizing the number and distribution of the three-dimensional radial basis function according to the residual drop rate in the expansion process, and performing basis function encryption on the key area by combining the LOS direction result of the reference point; an original two-dimensional state equation in a Kalman filtering algorithm is expanded to be three-dimensional, meanwhile, a GNSS three-dimensional observation value serves as a strong constraint condition of the Kalman filtering algorithm, and a final three-dimensional deformation rate field is output through iteration and 3D-Va analysis.

The invention relates to a processor and a terminal for positioning a subway train, and the processor is in communication connection with an inertial measurement unit of the train, and is used for receiving multi-source data, collected by the inertial measurement unit, of the train; in the processor, an initial correction module performs state estimation on original acceleration and angular velocity data of a train acquired by an inertial measurement unit based on an error Kalman filtering algorithm, and dynamically corrects measurement noise and system errors in combination with a motion model of the system and an actual measurement value of the inertial measurement unit; the curve generation module constructs a time-mileage curve of the train and corrects the time-mileage curve based on the first correction proportion; the turning recognition module calculates the total mileage in the turning section and corrects the total mileage in the turning section based on the second correction proportion of the turning section; the abnormal vibration recognition module recognizes abnormal vibration features based on the high-order change rate of the acceleration and the angular speed of the train and marks the abnormal vibration features in a visual mode.

The embodiment of the invention provides a time-space synchronization method and device, electronic equipment and a medium, which are used for solving the problem of inaccuracy of a local clock in related technologies. In the embodiment of the invention, the electronic equipment combines the average deviation pseudo-range, the RTT jitter and the target frequency offset into the multi-dimensional observation vector, so that the error source of clock synchronization is fully covered. The time deviation and the frequency deviation of the clock are accurately and effectively determined through a Kalman filtering algorithm, recursive processing of observation vectors and dynamic estimation of the time deviation and the frequency deviation of the clock, and high-precision synchronization of the local clock in a complex environment is achieved in combination with the optimization capability of the Kalman filtering algorithm. And the time of the local clock is the time determined according to the time of the multiple clocks, so that the clocks are more accurate.

The invention provides an urban low-altitude unmanned aerial vehicle risk assessment method based on LES-observation data. The method comprises the following steps: step 1, constructing a prior wind field database Kg; 2, collecting observation data y in real time; step 3, based on the set of the prior wind field database Kg matched with the current moment t and the observation data y of the current moment t, generating a set of posterior wind fields Xa by using a set Kalman filtering algorithm, and calculating an average posterior wind field Xaa; 4, predicting flight path risk evolution; 5, calculating a posterior wind field distribution diagram corresponding to each average posterior field Xaa; step 6, carrying out flight path risk assessment; and 7, circularly executing the steps 2-6 to realize risk dynamic assessment. According to the invention, wind field risk dynamic assessment on the flight path of the unmanned aerial vehicle is realized.

The invention belongs to the field of electrical variable measurement, and particularly relates to a single battery equalization management method for a lithium battery BMS (Battery Management System) protection board, which comprises the following steps of: acquiring voltage, temperature, total current and cycle index of each single battery in real time; based on a second-order equivalent circuit model and an extended Kalman filtering algorithm, multi-source information is fused, and the health state and the charge state are estimated with high precision; constructing a dynamic equilibrium trigger threshold fusing the charge state standard deviation, the range and the health state attenuation factor; a bidirectional flyback or multi-winding transformer active equalization strategy is selected according to the inconsistent distribution characteristics and the temperature field, and equalization current is accurately controlled through pulse width modulation; and continuously monitoring the efficiency, the temperature rise rate and the convergence in the equalization process, and dynamically optimizing the equalization parameters by using the fuzzy logic controller until the inconsistency reaches the standard. According to the technical scheme, accurate, self-adaptive, high-efficiency and safe single battery equalization management can be realized, the service life of the battery pack is effectively prolonged, and the operation reliability of the system is improved.

The invention discloses a bridge deformation monitoring method based on RTK differential positioning and IMU fusion and a medium, which fully considers the requirements for displacement, angle and other data in the bridge deformation monitoring process, realizes multi-angle and multi-azimuth monitoring by using GNSS and IMU, and improves the accuracy of bridge monitoring. The RTK positioning data and the IMU measurement data are fused through the extended Kalman filtering algorithm, the IMU error can be compensated, the advantages of the two kinds of data can be fully mined, and data complementation and optimization are achieved. Under the condition that satellite signals are shielded or interfered, IMU data can assist in maintaining the stability of a monitoring system, and interruption of positioning information is avoided; when the IMU data fluctuates for a short time, the high-precision positioning result of the RTK can correct the fusion result to ensure that the output bridge deformation monitoring data is always accurate and continuous, so that the robustness and reliability of the whole monitoring system in a complex environment are improved.

The invention is applicable to the technical field of concrete deformation measurement, and particularly relates to a non-contact concrete deformation testing system and method based on a laser ranging technology, and the system comprises a laser ranging unit which is provided with a double-frequency phase modulation laser, an optical transceiver antenna and a phase detection circuit; the environmental parameter acquisition unit comprises a temperature sensor, a humidity sensor and an air pressure sensor; and the data processing unit carries an atmospheric refractive index calculation module, a Kalman filtering algorithm and a three-level early warning module, and supports wired / wireless data transmission. The distance measurement precision is high, and the concrete micro-deformation monitoring requirement is met; non-contact characteristic: non-damage monitoring on the concrete surface is realized through a diffuse reflection target; intelligent data processing: structural deformation and environmental noise are effectively distinguished, and the early warning reliability is improved; automatic monitoring is achieved, and the manual inspection cost is greatly reduced.

The invention relates to the technical field of hydraulic engineering safety monitoring, and discloses a dam safety studying and judging method based on monitoring data multi-physics field simulation, which comprises the following steps: S1, multi-source data acquisition and time-space alignment; s2, dynamically updating the numerical model; s3, safety evaluation and early warning decision making; s4, performing multi-source risk coupling analysis; and S5, issuing the early warning information in a multi-mode manner. According to the method, time-space reference unification of multi-source monitoring data is achieved through feature point matching and sliding window cross-correlation analysis, a self-adaptive Kalman filtering algorithm is adopted to dynamically invert permeability coefficients and elastic modulus parameters, and boundary conditions of a finite element model are adjusted in combination with real-time water level changes; the dynamic simulation precision of a seepage field-displacement field-stress field coupling model is improved, the static evaluation limitation of a fixed threshold value method is broken through through a three-dimensional time-varying safety envelope surface and a Bayesian network grading early warning decision tree, and the risk prediction capability in the flood routing process is enhanced through multi-parameter joint probabilistic reasoning.

The invention provides a park integrated energy system low-carbon scheduling method combining demand response and carbon emission flow, and belongs to the technical field of energy system low-carbon scheduling. Establishing a directed weighted carbon flow network model based on a graph theory, tracking a carbon emission transmission path by adopting a maximum flow minimum cut theorem and a proportional allocation principle, constructing a space-time coupled dynamic carbon flow state space model, and performing state estimation by adopting a Kalman filtering algorithm; the carbon emission responsibilities are distributed based on a Shapley value method, a stepped carbon transaction cost function is established, an optimal scheduling strategy is solved through a double-layer iterative optimization framework, and the technical problem that the carbon emission responsibilities are difficult to distribute reasonably due to the fact that a park integrated energy system cannot accurately track a carbon emission transmission path when electric heat gas multi-energy flow coupling is considered is solved.