150 results about "Confidence factor" patented technology

The invention discloses a feed production equipment collaborative scheduling and operation optimization method based on deep learning. The method comprises the following steps: S1, constructing an equipment state sequence data set; s2, constructing a disturbance sequence data set; s3, inputting a disturbance resistance residual fusion network to generate a preliminary scheduling scheme; s4, constructing an equipment conflict reasoning graph; s5, embedding the equipment conflict reasoning graph into the scheduling network, and correcting the preliminary scheduling scheme; s6, inputting the corrected preliminary scheduling scheme into an improved NGBoost model, introducing a confidence factor estimation and dynamic distribution calibration mechanism, and outputting a predicted expected value and an uncertainty score of each scheduling behavior; s7, identifying a high-risk behavior according to the uncertainty score, and generating a scheduling correction candidate set; and S8, performing multi-target comprehensive evaluation on the scheduling correction candidate set, and screening the candidate set with the highest score as a final scheduling plan to be issued and executed. According to the method, deep learning and an improved NGBoost model are combined, and intelligent scheduling optimization of feed equipment is realized.

The invention relates to the technical field of energy management, in particular to a building energy-saving analysis system based on big data. According to the method, through time sequence segmentation of the real-time energy consumption data and the historical load records and in combination with Z-score standardization processing, accurate description of the dynamic fluctuation range of the building energy consumption data is achieved, and interference of seasonal changes and equipment operation fluctuation on an analysis result is effectively avoided. According to the outlier detection algorithm constructed based on multi-dimensional features, the fine granularity level of anomaly recognition is improved through composite analysis of curve slope and power fluctuation. A dynamic threshold self-adaptive mechanism is combined with an operation mode and an equipment state, so that the sensitivity and response adaptation capability of energy consumption data monitoring are enhanced. A multi-dimensional parameter fusion method constructed by an association index and a credibility factor is adopted, and the credibility modeling and risk grade division of the energy consumption data are effectively realized by quantifying the association among the fluctuation amplitude, the duration and the data deviation degree.

The invention discloses a weld quality intelligent diagnosis system based on deep learning, and relates to the technical field of welding quality detection, and the weld quality intelligent diagnosis system comprises an image quality evaluation module, a feature alignment module, a deviation detection module, a path reconstruction module, a prior enhancement module and a defect identification module, identifying an area of which the signal-to-noise ratio is lower than a preset threshold value, and constructing a noise interference distribution diagram; and the feature alignment module executes a deformable convolution feature alignment operation with a confidence factor adjustment mechanism based on the noise interference distribution diagram to generate an initial space mapping result. Through mechanisms such as image quality perception, robust alignment, deviation detection, self-adaptive reconstruction and prior enhancement, a closed-loop weld joint intelligent diagnosis process is constructed, false alignment errors are effectively inhibited, the multi-modal fusion stability and the defect recognition precision are improved, and the reliability and the intelligent level of the system under complex working conditions are enhanced.

The invention discloses a medical health cost prediction system and method based on multi-source data fusion, and relates to the technical field of medical data analysis, the system firstly fuses a plurality of heterogeneous medical information sources to generate structured data; then, multidimensional features related to the cost are extracted to construct a feature representation vector; and the cost prediction modeling module constructs a cost prediction model through graph structure modeling and semantic embedding, carries out joint optimization by combining a graph attention mechanism and semantic similarity, and outputs a prediction result by fusing a time sequence and static characteristics. A joint optimization algorithm of graph semantic comparison loss and prediction deviation loss is introduced into model training, and positive and negative sample pairs are constructed through a cost label distance. And the feedback optimization module triggers model updating when the prediction deviation exceeds a threshold value, and dynamically adjusts a model structure and parameters through a deviation index and a sample confidence factor. And the prediction interpretation module analyzes influence factors based on intermediate layer features or gradient propagation, outputs an interpretation report, and improves the transparency and credibility of the model.

A battery control component of an electric vehicle may limit charging of a battery on the electric vehicle to a target state of charge (SOC), such as to reserve battery capacity for regenerative braking during downgrades. A computing device associated with the vehicle may calculate an estimated gradient profile and / or an estimated total energy consumption of a predicted upcoming route of the vehicle based on one or more data items, such as GPS data, altitude data, and / or map data. The computing device may determine a target SOC based on the estimated gradient profile. The computing device may determine a confidence factor associated with the estimated gradient profile, where the confidence factor depends on the quantity of data items used to calculate the estimated gradient profile. The computing device may adjust the target SOC based on the confidence factor.

A method for monitoring temperature-induced deformation of components in an intelligent moxibustion robot based on infrared spectroscopy includes the following steps. Infrared spectral images and frequency spectra of a target component of the intelligent moxibustion robot at different detection points are acquired. Motion influence confidence factors for each detection position are constructed based on frequency differences between peaks and troughs in the frequency spectrum. The box-counting method is used to obtain scale-relationship graphs of infrared spectra for all detection positions. The overall light absorption difference index of the target component is determined according to the scale-relationship graphs. By combining the overall light absorption difference index with motion influence confidence factors, local outlier factors (LOF) for each detection position in the thermal data sequence are calculated using a LOF anomaly detection algorithm. Finally, a temperature deformation risk of the target component is evaluated based on a thermal alarm threshold.

The invention provides a landslide risk dynamic early warning method and system based on a Bayesian network, and belongs to the field of geological disaster intelligent prediction, and the method comprises the steps: constructing a disaster-inducing factor data set, and screening key factors based on a Pearson's correlation coefficient and an information gain method; an FP-Growth algorithm is further utilized to extract association rules among high-confidence factors, a Bayesian network structure is guided to be optimized, and a Bayesian network model with a causal relationship is constructed; the model supports an incremental learning mechanism based on a newly added landslide sample, can dynamically update a conditional probability table, and realizes landslide probability prediction and risk grade division in combination with a Bayesian forward reasoning result. The method has the advantages of being high in causal reasoning ability, high in model structure expression ability, excellent in prediction precision and capable of supporting real-time updating and risk partition, and is suitable for an intelligent risk assessment and early warning system for landslide disasters.

The invention relates to the technical field of wind turbine generator state monitoring, in particular to a wind turbine generator state monitoring method and system based on multi-source heterogeneous data fusion, and the system comprises a data collection unit, a dynamic noise processing unit, a damage feature analysis unit and a health state output unit. A data acquisition unit synchronously obtains blade strain, unit rotating speed and environment vibration signals through hardware timestamp alignment, and a dynamic noise processing unit removes rotating speed coupling noise by using a two-parameter coupling model, a temperature-pitch angle three-dimensional correction curved surface and closed-loop feedback in combination with variable step blanking and local band elimination protection. The damage feature analysis unit extracts microcrack features by adopting multi-scale wavelet packet decomposition and kurtosis detection, and performs cross validation by fusing a vibration mode confidence factor, and the health state output unit generates a topological graph containing a crack position, an expansion trend and an alarm confidence level, so that the problems of insufficient multi-source data fusion and false and missing judgment of damage are solved, and the safety of the system is improved. And the monitoring precision is improved.

The invention discloses a method and a device for evaluating the measurement uncertainty of an automatic verification line of an electric energy meter, and the method comprises the steps: analyzing each uncertainty source according to the structure of the to-be-evaluated automatic verification line of the electric energy meter and the environment condition of the to-be-evaluated automatic verification line of the electric energy meter, so as to determine each uncertainty component for evaluation; the same installation type standard electric energy meter is adopted to carry out a measurement experiment, data measured by the installation type standard electric energy meter is used as a reference value, and a relative error between the data measured by the installation type standard electric energy meter and data displayed by a working standard meter is used as a basic error of the automatic verification line of the electric energy meter. Calculating the value of each uncertainty component; taking the root mean square of the value of each uncertainty component as the synthesis standard uncertainty of the electric energy meter automatic verification line; and multiplying the synthesis standard uncertainty by a set confidence factor to obtain an evaluation result of the expansion uncertainty of the automatic verification line of the electric energy meter. According to the method, the measurement uncertainty can be evaluated more comprehensively and accurately.

A computer-implemented method for identifying objects in an environment is disclose. The method comprises: receiving image data representing the environment from one or more cameras; analysing the image data to detect objects in the environment and for each detected object, assigning a preliminary identification label and confidence factor to the object; receiving environmental data from one or more environmental sensors, analysing the environmental data to detect classification indicators and for each detected object, modifying the confidence factor assigned to the object if a classification indicator correlates with the preliminary identification label and / or using one or more measurement sensors to measure one or more physical characteristics relating to a set of objects at a predefined location within the environment, the set of objects being included within the detected objects, and, for each measured physical characteristic, modifying the confidence factor associated with each object in the set if the physical characteristic corresponds to an expected value for the objects found at the predefined location; and for each detected object, identifying the object by associating with it a final identification label equal to the preliminary identification label if the confidence factor assigned to the object meets a predefined criterion.

The invention relates to the technical field of image recognition, in particular to a fire behavior recognition method and system based on video monitoring. The technical problem that the early warning reliability of a video monitoring system on fire smoke is insufficient is solved. The method comprises the following steps: acquiring a monitoring video stream of a monitoring area and time sequence data acquired by a sensor in the monitoring area; determining an evaluation value of the to-be-detected area based on the image feature and the morphological change of the to-be-detected area; determining a confidence factor based on the form change trend of the to-be-detected region and the change trend of the time series data; determining a smoke authenticity value based on the evaluation value and the confidence factor; and adjusting an alarm threshold value of the smoke alarm based on the smoke authenticity value. The method is used for fire smoke early warning and monitoring scenes.

The invention relates to the technical field of industrial measurement and control, in particular to a multi-source data fusion system operation monitoring system which comprises a jump behavior recognition module, a confidence coefficient screening module, a behavior consistency extraction module, a power recovery anomaly detection module and an operation state monitoring output module. According to the method, the sudden change trend in the current and voltage signals of the operation equipment is extracted, the response dislocation condition between the signals is combined, the behavior change of the equipment can be accurately described, potential abnormal fragments are identified, the signal fragments are screened by means of the multi-dimensional confidence factor, and low-reliability data content is removed; the method comprises the following steps: establishing a characteristic time section with representativeness through time interlacing characteristics between triggering behaviors and behavior type consistency judgment, and realizing accurate marking of abnormal behaviors in a power recovery process of equipment by combining trend delay and fluctuation amplitude change of a power signal in a voltage rising process.

The invention provides a metal ore artificial intelligence sorting method based on lithofacies analysis, and relates to the technical field of data processing, and the method comprises the steps: obtaining a unified image of an ore sample, extracting a crystal structure contour, and calculating texture direction consistency, particle distribution density and color concentration degree in each region; constructing a lithofacies analysis model based on a two-channel neural network; training the lithofacies analysis model according to the training data set to obtain a fusion recognition model; obtaining an ore image to be sorted, and inputting the ore image to be sorted into the fusion recognition model to obtain predicted mineral distribution data; and extracting a boundary confidence factor and a region type probability, carrying out re-calibration on predicted mineral distribution data, generating a sorting control instruction, and driving sorting equipment to carry out physical sorting operation. The rock facies analysis is carried out on the ore image sample through artificial intelligence, and the types of symbiotic minerals can be accurately distinguished.

The invention discloses an underwater robot trajectory tracking control method. The method comprises the following steps: establishing an AUV motion model; establishing a nonlinear state space model; carrying out linearization and discretization on the nonlinear state space model; defining a prediction model to predict system behaviors, and performing dynamic optimization; designing an objective function based on model predictive control (MPC); an improved ant colony algorithm IACO is introduced, and a target function is solved by adopting a dynamic adjustment confidence factor and a transition probability constant. According to the underwater robot trajectory tracking control method provided by the invention, the trajectory tracking stability and precision of the AUV can be improved, and the task execution capability of the AUV in a complex underwater environment is improved.

The invention discloses a supplier material market price evaluation method and system based on AI networking search, belongs to the technical field of electronic commerce and data processing, and provides a double-weight confidence model based on time sequence fluctuation and statistical deviation, data are grouped according to platform types and suppliers, a price fluctuation oscillogram is generated for each group of data, and the price fluctuation oscillogram is calculated. Calculating average price and standard deviation in the group; the global average price is introduced, the confidence coefficient of a single group of data is dynamically quantified through a normalization formula, low weight is given to high-fluctuation and high-deviation data, noise interference is effectively suppressed, and the reference of the finally output optimized market price is improved; in addition, structured data with timestamps are generated and the grouping average price and confidence factor of each platform are synchronously output, so that purchasing personnel are assisted to transversely compare the quotation rationality of suppliers; and finally, the output optimized market price is fused with multi-source high-confidence data, so that enterprises are helped to accurately grasp the market price interval, and the bargaining capability and the purchasing efficiency are improved.

The invention provides an intelligent door and window control method and system, and the method comprises the steps: S1, obtaining multi-source environment data; s2, inputting the multi-source environment data into a fuzzification module, and converting the multi-source environment data into a fuzzy variable through a preset ambiguity function; s3, dynamically generating an environment parameter weight vector according to the real-time scene parameters; s4, inputting the fuzzy variable and the environmental parameter weight vector into a fuzzy inference engine, executing logical operation of a fuzzy rule base, and outputting comprehensive evaluation values of window opening suitability, window closing urgency and shading demand; and S5, generating a final control instruction through a defuzzification strategy, and executing the final control instruction. According to the method, the problem of frequent invalid operation caused by fluctuation of the environmental parameters near the boundary value is solved, the environmental parameter weight vector is dynamically generated based on the time parameters, the user preset mode and the meteorological data confidence factor, adaptation to seasons, time periods, user states and other scenes is achieved, and decision logic can be flexibly adjusted according to actual requirements; and a contradictory decision under a traditional fixed rule is avoided.

The invention provides a ship docking positioning method and system based on multi-source positioning data fusion, and relates to the technical field of data processing, and the method comprises the steps: obtaining the multi-source positioning data of a ship; projecting the multi-source positioning data into a unified local coordinate system through coordinate transformation; performing consistency check on the positioning data after coordinate transformation, and determining consistency factors of various positioning data; judging whether the consistency factor of the various positioning data is greater than a consistency threshold value, and if so, keeping the consistency factor in the current measurement period; otherwise, removing in the current measurement period; determining confidence factors of the various reserved positioning data according to the historical performance of the reserved positioning data; and based on the confidence factors of various positioning data, carrying out weighted fusion on the multi-source positioning data through a Kalman filtering algorithm, and determining fusion positioning data of the ship. The ship docking positioning precision and reliability can be improved, the anti-interference capability of the system is enhanced, and the system adapts to the dynamically changing environment.

The invention belongs to the technical field of power distribution automation of a power system, and particularly relates to a self-adaptive load control method and system for an intelligent cable branch box, and the method comprises the steps: collecting and preprocessing the current data of a branch loop and the temperature data of a cable joint in real time, obtaining a data confidence factor, and extracting a current change rate and a temperature rise rate; obtaining a load prediction value in a future set time period through a load trend prediction model; calculating the residual heat tolerance time for reaching the limit tolerance temperature; calculating an output load control adjustment coefficient based on the adaptive load control decision model and a particle swarm optimization algorithm; and judging a current state interval according to the load control adjustment coefficient, and controlling an execution end to execute a grading response strategy of through-flow maintenance, short-time overload monitoring and early warning and breaking current limiting in combination with the residual heat tolerance time. According to the method, the residual heat tolerance time is calculated in combination with load prediction and a transient thermal circuit model, and hierarchical response of load control is realized through a particle swarm optimized radial basis function neural network decision.

The invention discloses an underground safety level intelligent evaluation response method and system fusing fuzzy credibility weighting and Bayesian reasoning, and relates to the technical field of underground safety monitoring. According to the method, a multi-source heterogeneous sensor is deployed to collect data, a fuzzy membership matrix is obtained through fuzzy membership function normalization, a credibility factor matrix is constructed by combining time sequence stability, spatial neighborhood consistency and node long-term health weight, and the two are subjected to weighted fusion to obtain a fusion index vector. And inputting the Bayesian network fused with the credible nodes, reasoning to obtain security level posterior probability distribution, and triggering a linkage response. The system correspondingly comprises a plurality of modules for implementing the steps. According to the method, the dynamic evaluation and credibility quantification of the data quality of the underground multi-source sensor are realized, and an intelligent evaluation mechanism for deeply integrating the credibility of the data source into a reasoning structure is constructed, so that the risk evaluation robustness and accuracy are improved, the closed-loop management from evaluation to response is realized, and the underground safety is guaranteed.

The invention discloses a freight logistics track abnormity processing method and system, and belongs to the technical field of intelligent logistics monitoring, and the method comprises the steps: obtaining a track original data set of a target freight vehicle; constructing a behavior evolution model based on the historical trajectory data, wherein the behavior evolution model is used for describing conventional operation boundaries of the vehicle under different road types and time periods; performing multi-source fusion correction on the trajectory data to generate a trajectory point sequence; calculating credibility factors of the track points based on GPS stability, historical track goodness of fit and road matching degree; constructing an anomaly judgment graph, and generating an anomaly distribution function by adopting a recursive scoring mechanism; identifying a track abnormal fragment based on a dynamic adaptive threshold value and outputting an abnormal label; generating a scheduling suggestion according to the abnormal label and a vehicle scheduling rule, and pushing the scheduling suggestion to a vehicle-mounted system in real time; according to the invention, individuation, high precision and real-time performance of track abnormity identification are realized, and the method is suitable for an intelligent logistics management and dynamic scheduling system in a complex scene.

The invention discloses an aircraft performance constraint compliance control method for contact type nondestructive testing, and belongs to the field of aircraft control. The implementation method comprises the following steps: introducing a saturated contact force controller into a position ring, and dynamically adjusting a confidence factor based on a position error to adaptively adjust normal rigidity and tangential flexibility; a zero-order control barrier function ZOCBF restrains the force controller, a three-dimensional dynamic safety boundary is established by using a sampling data mechanism, and the position, speed and contact force of the unmanned aerial vehicle are optimized and limited through quadratic programming; designing a hybrid torque controller in the attitude ring, and dynamically selecting a balance point through a binary logic switching strategy; the controller performs safety limitation on the attitude and angular velocity of the unmanned aerial vehicle in combination with zero-order control obstacle function constraint; monitoring a quaternion scalar component symbol in real time by defining a manifold switching set and a jump set; by fusing position loop and attitude loop control logic, compliance control during contact-type nondestructive testing of the unmanned aerial vehicle is realized.

The invention discloses an automatic modal parameter identification method based on a random subspace method and DBSCAN clustering. The method comprises the following steps: arranging an acceleration sensor on a to-be-measured structure to obtain a structure dynamic response; performing modal calculation based on a covariance random subspace method of a time domain to obtain an original stability graph; obtaining a preliminarily cleaned stable graph based on a hard index criterion, and superposing and drawing the average regularization power spectrum density of a frequency domain; all the poles are used as input samples of DBSCAN clustering, and the minimum neighborhood sample number is determined; calculating a normalized modal distance between any two poles; calculating a modal distance and distribution to determine a neighborhood radius value; substituting into DBSCAN clustering to obtain a noise-rejected stability graph; on the basis of linear normalized modal energy and clustering cluster dimensions, performing bisection K-means clustering to screen clusters of candidate physical modals; evaluating modal splitting according to the modal confidence factor MAC and the modal overlap factor MOF; a representative modal of the damping ratio median index is extracted and modal parameters are identified. The invention further discloses a system, electronic equipment and a computer readable storage medium.

An infrared-spectroscopy-based method for monitoring temperature-induced deformation of a component of an intelligent moxibustion robot. The method comprises: collecting an infrared spectrum and a frequency spectrum of each detection position on a target component of a moxibustion robot; on the basis of frequency differences between peaks and troughs in the frequency spectrum, constructing a motion influence confidence factor of each detection position; using a box-counting method to acquire a scale relationship graph of the infrared spectrum of each detection position; on the basis of scale relationship graphs of all the detection positions, determining an overall absorbance difference index of the target component; on the basis of the overall absorbance difference index and the motion influence confidence factor, using a local outlier factor (LOF) outlier detection algorithm to calculate an LOF of each detection position in a thermal data sequence; and on the basis of a thermal alarm threshold value, determining a temperature-induced deformation risk of the target component. The method can eliminate spectral difference variations caused by shadows and reflectance properties, thereby improving the precision of determining high-temperature deformation of components.

The invention relates to the technical field of intelligent power grid abnormity diagnosis, in particular to an intelligent electric energy meter operation data abnormity diagnosis method and system. The method comprises the steps of establishing a sliding window, collecting magnetic induction intensity and load current data, maintaining references of the magnetic induction intensity and the load current data according to historical data, calculating the change direction and intensity of a sampling point relative to the references, screening data with enhanced magnetic induction intensity and reduced load current, and carrying out weighted covariance calculation by utilizing an effective mask. Constructing a negative correlation response coupling index; constructing an abnormal definite diagnosis confidence reasoning model; calculating a causal confidence factor based on the coupling index; calculating an aging confidence factor based on the duration of an abnormal state; according to the scheme, industrial interference and electricity stealing attacks can be accurately distinguished, hidden attacks are captured, the false alarm rate is reduced, and diagnosis blind areas are eliminated.

A robot may include a processor, configured to operate according to a first operational mode or a second operational mode, wherein the first operational mode comprises performing an environmental perception operation using first sensor data, wherein the first sensor data comprise no camera data, and wherein the second operational mode comprises performing the environmental perception operation using second sensor data, wherein the second sensor data comprise camera data; while operating according to the first operational mode, determine a confidence factor representing a confidence of the environmental perception operation; determine whether or not the confidence factor is outside of a range; and if the confidence factor is outside of the range, operate according the second operational mode.

The invention provides a small-well-spacing horizontal well cluster well pattern parameter generation method and device, equipment and a medium, and relates to the technical field of horizontal well cluster well pattern development. The method comprises the following steps: acquiring well cluster layout demand parameters and ranging tool parameters; determining the trajectory control tolerance of the top well in the vertical direction according to the well cluster layout demand parameters; determining a magnetic ranging error according to the ranging tool parameters; according to a preset ranging error confidence factor, a preset well track control error, a track control tolerance, a magnetic ranging error and a well cluster layout demand parameter, determining the number of wells in a well pattern; according to a preset distance measurement error confidence factor, the magnetic distance measurement error, a preset well track control error, the well number of the well pattern and the well cluster layout demand parameters, determining well pattern lateral deviation; and performing well pattern layout processing according to the well number of the well pattern, the lateral deviation of the well pattern and the well cluster layout demand parameters, and determining well pattern parameters of the horizontal well cluster. When the well pattern of the small well spacing horizontal well cluster is designed, the accuracy of determining the well pattern parameters is effectively improved through the method.

Exemplary systems and methods are directed to reducing processing complexity for command and control of a remote autonomous object in response to natural language commands. A processor receives a user command in a natural language format and extracts designated parameters for matching with a control prompt stored in a library of control prompts. The processor determines a confidence from a result of the operation. The confidence factor is used to selectively route the user command to a simple command processing path to generate a simple command message or a complex command processing path having one or more neural networks to generate a complex command message. The processor compares the simple command objective message, or the complex command objective message generated from the selective routing operation with known capabilities of the remote autonomous object to identify a remote autonomous object command, which is formatted and transmitted to the remote autonomous object.

The invention discloses a natural road feature-based vehicle-mounted radar external parameter calibration method and device, a storage medium and a program product. The method comprises the steps of obtaining synchronous radar point cloud data and visual image data; determining a visual lane line according to the visual image data, and determining a radar lane line according to the radar point cloud data; lane line transverse deviation is calculated according to the visual lane line and the radar lane line, and deviation confidence is calculated according to the credibility factor and the factor weight; determining a deviation estimated value based on the effective deviation and the deviation confidence by adopting a one-dimensional Kalman filter; the deviation estimation value is evaluated according to preset calibration threshold values, the preset calibration threshold values comprise the minimum calibration threshold value and the maximum calibration threshold value, when the deviation estimation value is larger than the minimum calibration threshold value and the maximum calibration threshold value, the radar coordinates are calibrated based on the deviation estimation value, and when the deviation estimation value is larger than the maximum calibration threshold value, an early warning mode is started. And outputting early warning prompt information in response to the early warning mode, and recording a fault code.

The invention relates to the technical field of robot navigation, in particular to a robust visual inertial navigation method for dynamic and static factor decoupling in a high dynamic environment, and the method comprises the following steps: obtaining prior pose information and visual feature data of an inertial measurement unit from a navigation system; the method comprises the following steps: generating a stable confidence factor and a stable inertia factor of a visual feature based on priori pose information and visual feature data of an inertial measurement unit, and generating a double-factor residual error at the same time; based on prior pose information and visual feature data of the inertial measurement unit, effective dynamic features are identified, and a dynamic effect residual error is generated; and in combination with the basic residual error, the two-factor residual error and the dynamic effect residual error of the navigation system, decoupling dynamic and static residual errors and carrying out joint optimization to obtain an optimized navigation track. According to the method, the robustness of the visual inertial navigation system in a complex environment can be improved through dynamic and static factor decoupling and dynamic compensation constraint, and scene specific training is not needed.

The invention relates to the technical field of main power distribution network monitoring, in particular to a main power distribution network integrated dispatching monitoring method and system, and the system achieves main power distribution network integrated dispatching monitoring through the method steps of main network and distribution network data acquisition, data preprocessing, data fusion, main power distribution network fusion topology model establishment and the like. When the operation data of the main power distribution network is fused, the traditional Bayesian fusion algorithm is improved by using a confidence factor, so that the heterogeneous data sources can be more effectively integrated, and more accurate information is provided for the operation and maintenance of the main power distribution network; the fused state estimation value reflects the most probable value of the state parameter of the main power distribution network under the condition that all observation data and priori knowledge are given, the information of all data sources is integrated, and the reliability of different data sources is constrained through confidence factors, so that the state estimation value is more accurate and reliable.