981 results about "Filter methods" patented technology

The invention relates to the technical field of traffic filtering, and discloses a CC attack feature-based traffic filtering method and device. The method comprises the following steps: performing feature extraction and Laplacian feature mapping dimensionality reduction on a historical HTTP request traffic sample to obtain a dimensionality-reduced feature vector set; cC attack feature analysis is carried out based on the dimension reduction feature vector set, and a CC attack feature parameter set is obtained; performing attack risk assessment on the real-time HTTP request to be filtered based on the CC attack feature parameter set to obtain an attack possibility score; performing time-varying parameter defense analysis based on the attack possibility score to obtain an adaptive defense rule set; and performing hierarchical filtering processing on the real-time HTTP request to be filtered according to the self-adaptive defense rule set to obtain filtered security traffic. According to the method, the defense parameters can be dynamically adjusted according to the attack situation, compared with a static defense rule, the method is more flexible and adaptive, and the resource utilization efficiency can be optimized while safety is guaranteed.

An adaptive variable step-size LMS filter based on a hyperbolic tangent function and its filtering method and computer equipment belong to the field of filter technology, and solve the problem that the LMS algorithm improved based on a nonlinear function requires too many manual adjustments of parameters and that the convergence speed and convergence accuracy of the traditional LMS algorithm cannot be satisfied simultaneously. The method of the present invention includes: determining the filter tap length and the number of iterations; initializing the filter parameters; obtaining the filter input signal vector; determining the instantaneous expected signal of the filter; obtaining the instantaneous output signal; obtaining the instantaneous error signal of the filter and the accumulated amount of the error signal based on the instantaneous output signal; obtaining the instantaneous step size using a step-size update formula based on a hyperbolic tangent function; and continuously iterating the filter tap weight coefficient vector to construct an LMS filter using the filter tap weight coefficient vector obtained after the number of iterations. The present invention is suitable for improving the performance of the LMS filter, and the filter of the present invention can be widely used in various signal processing fields.

The invention relates to the technical field of signal processing, in particular to an unmanned aerial vehicle self-noise filtering method and system based on multichannel time-frequency spatial filtering. The method comprises the following steps: acquiring a multi-channel noise-containing sound signal by using a microphone array carried by an unmanned aerial vehicle, and performing short-time Fourier transform to obtain a frequency domain signal; estimating a ratio mask by using a deep neural network, and performing logarithm normalization processing on the frequency domain signal; remodeling the sound signal to match the FFT length of the time-frequency spatial filter, and constructing the time-frequency spatial filter in combination with the multi-channel ratio mask; removing the self-noise of the unmanned aerial vehicle by using the filter and obtaining a target sound signal; and finally, converting the frequency domain signal into a time domain signal through inverse short-time Fourier transform. Through innovative fusion of multi-channel acquisition, deep learning and time-frequency space filtering, the self-noise of the unmanned aerial vehicle in a complex environment is accurately filtered, and the accuracy and efficiency of transformer substation noise monitoring are remarkably improved. Reliable acoustic data are provided for state monitoring of the power equipment, and the method has important engineering application value.

The present invention relates to a Livox laser radar-based dynamic object filtering method for a three-dimensional point cloud map. The method comprises: saving point clouds and corresponding poses of all frames of a mobile robot during mapping, setting the saved point cloud of each frame as a query frame, and converting point cloud data of the query frames and of a point cloud map into a fan-shaped grid cluster point cloud descriptor represented by local height extreme value difference information; on the basis of the fan-shaped grid cluster point cloud descriptor, by means of a region-adaptive point cloud ground extraction method, segmenting to obtain a ground point cloud estimation result; and obtaining a potential dynamic region by means of a descriptor comparison method, and on the basis of the ground point cloud estimation result, replacing a dynamic point cloud in an original point cloud map with a ground point cloud segmented in a two-dimensional grid in the query frames. The present invention can eliminate smears generated by dynamic objects in existing three-dimensional point cloud maps, and obtain a static point cloud map allowing for robot positioning and autonomous navigation.

Methods for video decoding and encoding, apparatuses and non-transitory computer-readable storage media thereof are provided. In one method for video decoding, a decoder may obtain a plurality of prediction blocks based on a current inter coding block; obtain a current template of the current inter coding block, wherein the current template comprises a plurality of reconstructed samples neighboring to the current inter coding block; obtain a plurality of template predictions of the current template respectively corresponding to the plurality of prediction blocks of the current inter coding block; obtain at least one filter based on the plurality of template predictions and the current template; and obtain a filtered block based on the at least one filter and the plurality of prediction blocks.

The invention provides a multi-stage progressive enhanced knowledge retrieval method for industrial process operation and maintenance, and relates to the field of natural language processing and information retrieval, and the method comprises the following steps: constructing a knowledge base in the field of industrial process operation and maintenance; obtaining user query and classifying; screening the documents in the knowledge base through the classified user query and a BM25 algorithm, and determining a first candidate document; screening the first candidate document through a lightweight filtering method combining TF-IDF word frequency-inverse document frequency and cosine similarity to obtain a second candidate document; the second candidate documents are accurately sorted through retrieval based on dense vectors, answers most matched with user query are obtained, the retrieval process is optimized layer by layer through retrieval strategies of four stages of query routing, keyword recall, lightweight filtering and vector fine arrangement, and therefore the retrieval efficiency and the retrieval precision are improved.

The invention discloses a text and image fused online comment toxicity detection and filtering method and system, and relates to the technical field of natural language processing, and the method comprises the steps: extracting a semantic vector of a comment text through a RoBERTa-Marge model; the visual features of the image are extracted through an OfficientNet-V2 model; aligning heterogeneous modal features by adopting a double-flow contrast loss function; self-adaptive decision making of culture sensitivity: loading a regional sensitive rule table according to a user IP address, and dynamically adjusting symbolic semantics; calculating an intimacy correction factor based on the social relationship between the publisher and the receiver; and performing context weighted toxicity scoring, calculating a user historical behavior weight, and outputting a final toxicity probability. According to the method, a deep dynamic mapping mechanism of text and visual features is constructed, heterogeneous features are extracted through RoBERTa-Large and OfficientNet-V2 double-flow architectures, semantic space alignment is forced by utilizing comparative learning, and the problem of image-text splitting detection in the traditional technology is solved.

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.

The invention discloses a multi-scale hierarchical efficient filtering method considering topographic features, which can efficiently extract a large number of uniformly distributed accurate ground seed points and ensure the accuracy of an initial topographic surface by introducing a method of combining a sliding grid technology and an M estimation sample consistency algorithm. Then, constructing a ground reference surface grid according to the ground points subjected to iterative recognition, and quickly generating a ground reference surface by adopting a grid-based global weighted finite difference TPS method; and finally, establishing a self-adaptive threshold value considering topographic features to capture ground points, thereby effectively improving the point cloud filtering precision in a complex scene. The filtering method is stable in performance and good in filtering effect in a large-scale scene with complex terrain and mixed ground features, and the filtering method is high in filtering precision, high in calculation efficiency and high in terrain adaptive capacity. Meanwhile, the filtering method is high in operation speed and has the performance advantage of processing large complex scenes.

The invention discloses a radar and visual target association fusion tracking cross-frame integration method in a traffic scene, which comprises the following steps of: firstly, acquiring original data of a sensor and pre-processing to generate aligned radar and visual targets, establishing prior fusion under the condition that the targets are not associated through complementary sensor target characteristics, the method comprises the following steps: acquiring a priori fusion set as an anchor point, establishing cross-frame anchor point matching between the priori fusion set and a traffic target tracking track, decoupling an anchor point matching result to generate a local target global association result under cross-frame constraint, and finally realizing secondary fusion of a radar and a visual target under a one-to-one condition by using a fusion criterion. And based on a filtering method, filtering updating fusion of radar and vision is realized, multi-target tracking is completed, and a track of a traffic participation target and a filtering fusion result are output. According to the method, the influence of the problems of target loss, deviation and the like of a single sensor on the traffic target tracking track stability in a traffic scene is effectively reduced, and the capability of continuously and accurately sensing the traffic target is remarkably improved.

The invention relates to the technical field of sensitive word filtering, in particular to a sensitive word filtering method and device based on multiple modes. The method comprises the steps of obtaining to-be-detected data; wherein the to-be-detected data at least comprises one piece of to-be-detected image data and one piece of to-be-detected text data; performing feature extraction on the to-be-detected data to obtain image features and text features; mapping the image features and the text features to a multi-modal causal knowledge graph to obtain a to-be-detected causal knowledge graph; wherein the to-be-detected causal knowledge graph is used for representing a sensitive relationship between a to-be-detected image and to-be-detected text data in the to-be-detected data; according to the method, sensitive word filtering is performed on the to-be-detected data based on the to-be-detected causal knowledge graph, so that the sensitive words in the text are accurately filtered.

The invention relates to the technical field of satellite internet, and discloses a low-orbit satellite phased array multi-beam interference modeling and suppression method and system, and the method comprises the steps: selecting a Kaiser window as a core filtering method, and achieving the optimization of beam characteristics through the dynamic adjustment of a shape parameter beta; generating an initial beam directional diagram based on a digital phase matching method, and multiplying the Kaiser window function coefficient by the excitation weight of the 64-array-element linear array element by element to realize spatial domain weighted filtering; the method comprises the following steps: constructing a training data set containing multi-scene interference characteristics, calculating a corresponding covariance matrix and an accurate inverse matrix thereof to form a sample pair, designing a deep neural network architecture, inputting a flattened covariance matrix vector, and learning a complex nonlinear mapping relation from the covariance matrix to the inverse matrix through a multi-layer full-connection structure; a mean square error is used as a loss function to constrain network output precision, and a multi-beam interference system model is constructed; according to the invention, stable and efficient communication of the low-orbit satellite system in a complex electromagnetic environment and under rapid channel change is ensured.

The invention provides a gas abnormal working condition monitoring and alarming system, relates to the field of gas monitoring and early warning, and adopts a monitoring system comprising a data preprocessing module, a spatial-temporal characteristic module, an ST-CGN network module, an edge intelligent optimization module and a result processing module. Wherein the data preprocessing module comprises a sliding window fragmentation method, a missing value interpolation method, a noise filtering method, a normalization method and a coding method; the spatial-temporal feature module extracts spatial correlation features, time dynamics features and causal propagation features, and fuses and splices the features into a three-dimensional feature tensor; the ST-CGN network module outputs a multi-working-condition probability vector corresponding to a sliding window fragment through the input of a three-dimensional feature tensor; and the edge intelligent optimization module realizes edge optimization of the ST-CGN network module. The system is used for monitoring and early warning gas abnormal working conditions, solves many problems existing in traditional gas pipe network monitoring, and has the advantages of being rapid in response, high in monitoring precision, high in cross-scene generalization ability and the like.

The invention discloses an IIR (Infinite Impulse Response) filter device and a filtering method thereof, and the IIR filter device comprises a head data module which stores current input data and two pieces of input historical data; the filtering module is used for storing output data of each stage through a first position, corresponding to each stage of filtering, of the intermediate data module, exchanging addresses of the first position and a second position, reading current input data and two pieces of input historical data from a specified position through the calculation module, and outputting the data; two pieces of output historical data are read from the intermediate data module and stored in the tail data module, data are output after filtering, and lower-level filtering is triggered; the tail data module is used for storing the output data of the level of filtering as the input data of the lower level of filtering when any level of filtering is completed; wherein the designated position of the first-stage filtering is a head data module, and the designated positions of the other-stage filtering are tail data modules. According to the method, the data reading and writing time is shortened, the calculation pipeline is prevented from being interrupted, the required data storage is reduced by half, and the required clock frequency is reduced.

The invention relates to the technical field of intelligent control, in particular to a coke oven gas collecting tube pressure dynamic adjusting method and system based on intelligent control, and the method comprises the steps: obtaining pressure data from a gas collecting tube through a sensor, and storing the pressure data in a time sequence format to obtain a pressure deviation initial sequence; according to the pressure deviation initial sequence, calculating a data difference value and carrying out smoothing processing to obtain a change rate sequence; analyzing the change trend of the change rate sequence, and calculating a secondary difference value to obtain a change acceleration sequence; according to the change rate sequence and the change acceleration sequence, predicting a pressure deviation development trajectory by adopting a filtering method to obtain a predicted deviation trajectory; and if the predicted deviation trajectory exceeds a preset threshold range, obtaining a deviation influence evaluation value through historical data comparative analysis. The problem that the pressure control precision of the coke oven gas collecting pipe is poor in the high-temperature and high-pressure environment is solved, and the pressure adjusting precision of the gas collecting pipe in the high-temperature and high-pressure environment is improved.

The invention discloses a thick vegetation coverage area point cloud filtering method and system based on implicit neural representation, and belongs to the technical field of point cloud filtering, and the method comprises the steps: obtaining point cloud data, building a self-adaptive irregular grid according to the point cloud data, segmenting the point cloud data based on the self-adaptive irregular grid, and obtaining the point cloud data of a plurality of sub-regions; inputting the point cloud data of each sub-region into an up-sampling framework based on an implicit neural network to obtain global point cloud data; and carrying out filtering processing on the global point cloud data based on a multi-scale feature point cloud filtering algorithm, and outputting a global point cloud ground point set. By setting an adaptive irregular grid, the density of the grid is automatically adjusted, so that the fine segmentation of the point cloud data of the thick vegetation coverage area is realized; an upsampling framework based on an implicit neural network is adopted to solve the sparsity of the point cloud; a multi-scale feature point cloud filtering algorithm is used for filtering global point cloud data, and the filtering effect on ground objects and vegetation at all levels is improved.

The invention discloses a GRACE and Swarm time-varying gravity field fusion filtering method based on a state space model, and the method comprises the steps: taking a spherical harmonic coefficient as a state quantity, constructing a random walk process equation, introducing three types of observations, employing a quantization parameter for observation noise and process noise covariance, constructing according to an order / power law, and carrying out the self-adaptive updating along with the monthly; a Nelder-Mead method is adopted to search for spectral index parameters, and an EM algorithm and a statistical method are utilized to update other parameters in a closed / quasi-closed mode; obtaining the state and posterior covariance of a full time sequence by using Kalman filtering and RTS smoothing; in the GRACE and GRACE-FO window period, continuous reconstruction is carried out by means of a process model and Swarm; and outputting quality evaluation information including monthly gravity field coefficients, posterior covariance, innovative variance ratio, residual whitening test, space power spectrum, uncertainty band and the like. According to the method, while physical rationality and calculation feasibility are ensured, a continuous and stable monthly time-varying gravitational field sequence with quantifiable uncertainty is realized.

The invention discloses a vulnerability information knowledge graph construction method and a vulnerability patch acquisition method, and belongs to the technical field of computer program detection. According to the method, the vulnerability information related to the current vulnerability is collected as much as possible through a vulnerability information collection method based on search and used for constructing the vulnerability information knowledge graph, and then one or more patch codes related to the current vulnerability are mined and submitted through a screening method based on a white list and a filtering method based on a black list. Finally, more patch code submission related to the current vulnerability is obtained through vulnerability repair code submission similarity analysis, the purpose of finding all vulnerability patches is achieved as much as possible, and finally a security patch code submission set of the vulnerability number is output. According to the method, the patch mining accuracy can be improved, meanwhile, the patch with the complete vulnerability can be efficiently mined under the conditions that single-code submission and multi-code submission exist in the vulnerability patch, and the limitation and defects of an existing vulnerability patch mining method are overcome.

The invention discloses a personalized cross-domain recommendation method and system based on federal learning, and the method provides a personalized cross-domain recommendation service for a user on the premise that original data of interaction between the user and an article and user parameters are kept locally. The method comprises two stages of federated training: stage 1, intra-domain users cooperatively train a single-domain score prediction model by using a neural collaborative filtering method; in the second stage, overlapping users of the two domains cooperatively train a migration module based on a multi-layer neural network to capture a mapping relation represented by potential user features between the two domains; besides, each layer of network of the cross-domain recommendation model is decomposed into a base vector and a personalized vector which respectively represent common knowledge among different users and unique knowledge of the users, and a local model obtained by final training can provide personalized recommendation services for registered users in a target domain, so that the recommendation efficiency is improved, and the user experience is improved. Meanwhile, the global model obtained through training provides effective initial recommendation for new users in the target domain, and the cold start problem in a recommendation algorithm is effectively relieved.

The invention discloses an evolutionary multitask intrusion detection feature selection method based on double-view dimension reduction, and belongs to the crossing field of network security and machine learning. According to the method, simplified and complementary tasks are generated through an improved double-view-angle dimension reduction method based on a filtering method and a grouping method, and rapid recognition of foreground areas in a high-dimensional search space is promoted; and through a multi-task optimization mechanism based on double archives, feature subsets with the same performance are maintained, convergence guidance is provided, and the balance between convergence and diversity among tasks is realized, so that a plurality of equivalent feature subsets with high intrusion detection accuracy and fewer feature numbers are searched and obtained, and the accuracy of intrusion detection is improved. The accuracy and the training efficiency are improved, and meanwhile, diversified and more interpretable decision support is provided for the model. According to the method, the Pareto optimal feature subsets with the same performance but different features can be quickly searched in the high-dimensional feature space, and more flexible and diversified selections are provided for decision makers, so that the accuracy of intrusion detection is improved, and the model training time is shortened.

The invention belongs to the technical field of information security, and particularly relates to an unmanned surface ship fusion filtering method with sensor saturation under FDI attack. Constructing a discrete time multi-sensor network system model, constructing a real number matrix inequality optimization filter design based on linear and nonlinear functions, and enabling filtering to show exponential stability under the mean square meaning; and according to local filter parameters, a distributed fusion filter is designed to resist FDI attacks. The anti-interference capability and the data fusion precision of the unmanned surface ship in a severe environment are improved by using an optimization design and a distributed strategy.

The invention discloses a geometric constraint fitting point cloud filtering method for sea surface three-dimensional reconstruction, and belongs to the technical field of computer vision and three-dimensional reconstruction. The objective of the invention is to solve the problem of insufficient subsequent three-dimensional reconstruction precision caused by interference of reflection noise, mismatching points and the like in sea surface point cloud. The method specifically comprises the following seven steps: firstly, acquiring sea surface original point cloud through three-dimensional data acquisition equipment; a filtering technology is adopted to obtain a to-be-fitted point cloud; fitting a quadric surface through an improved RANSAC (Random Sample Consensus) algorithm to solve an initial parameter; constructing a comprehensive error function, and optimizing the model through gradient descent; effective inner points are screened through quadratic term coefficient constraint and a distance threshold value; and finally, iterating until a termination condition is met, and outputting an optimal effective point cloud. The method is high in noise rejection rate, the point cloud fits the sea surface form, and high-quality data support can be provided for sea surface fitting, sea wave simulation and unmanned ship control.

The invention discloses a flow filtering method and device for extracting quintuple flow characteristics, and the method comprises the steps: obtaining a quintuple characteristic sequence of a historical network communication data sample; in a plurality of time windows, extracting joint probability distribution among different quintuple features in the quintuple feature sequence to obtain time sequence correlation features; constructing a communication prompt word according to the time sequence association feature; inputting the communication prompt word into a preset large language model for training to obtain a target large language model; performing anomaly analysis on the real-time network communication data through the target large language model to obtain an analysis result; determining an exception type based on an analysis result; and determining a traffic filtering strategy according to the exception type and a preset strategy library, and performing traffic filtering through the traffic filtering strategy. According to the invention, the accuracy of abnormal traffic filtering can be improved.

The invention relates to the technical field of three-dimensional image calculation processing, in particular to a large-scale scene data-oriented point cloud filtering method, which comprises the following steps of: constructing a point cloud filtering network SC-CNN on the basis of a multi-layer perceptron (MLP) network Point Net; a gradient constrained farthest point sampling module is used for carrying out adaptive subregion division, gradient pre-classification and subregion sample sampling on an original point cloud, and a Copula correlation and attention mechanism intra-class point feature enhancement module is combined to take a ground point and a non-ground point in the point cloud as variables; the Copula model is used for carrying out quantitative analysis on the correlation of variable data to enhance the capability of learning classification features from sparse point clouds, reduce classification errors to the greatest extent and improve the overall filtering precision; and finally, combining the intra-class points verified by Copula with the calibrated ambiguous points by combining a Copula correlation and confidence coefficient estimation classification error correction module to obtain a filtering result.

The invention discloses a robustness tracking filtering method for group targets, and belongs to the field of radar and signal processing. The implementation method comprises the following steps: establishing a kinetic model of a group target and a three-coordinate ground-based radar detection probability and measurement model; bayesian recursion of a multi-target state random finite set is realized by a Poisson random finite set, and an intensity function of the Poisson random finite set is described in a Gaussian mixture form. Estimating the maximum motion distance of the target between two adjacent filtering steps based on the dynamic characteristics of the group target, and achieving the detection of the target; the detection probability is described by using Bernoulli distribution, under the condition that the clutter intensity does not exceed the real target intensity, the prior clutter intensity is taken as a threshold value, the detection probability is adaptively adjusted in the filtering iteration process, the respective weights of the measurement irrelevant part and the measurement relevant part are updated, and the strong-robustness multi-target tracking under the condition of sensor leak detection is realized. The method also has the advantages of strong robustness, high tracking precision and small calculation amount.

The invention belongs to the technical field of hidden danger detection, and more specifically relates to a power transmission hidden danger filtering method and system fusing a visual model and spatial logic correction, and a medium. The method comprises the following steps: hidden danger candidate generation based on a high-performance target detection model: adopting a reference segmentation image dynamic multiplexing strategy to complete background semantic analysis of a power transmission channel inspection image, and generating a final reference segmentation image as a semantic mask; performing spatial mapping and logic correction on hidden danger candidate boxes and semantic masks; performing confidence weighting and soft filtering on boundary cases; identifying the boundary cases, constructing a three-dimensional confidence fusion model to calculate fusion confidence, realizing soft filtering according to the fusion confidence, and outputting a high-credibility hidden danger alarm list. The problems that in the prior art, due to the fact that a background semantic base is unstable and total image re-segmentation is frequently triggered, computing resources are wasted, scenes violating common sense cannot be recognized, and the uncertainty of a boundary area is lack of fine-grained description are solved.

The invention provides a gateway security filtering method, electronic equipment, a storage medium and a program product. The method comprises the following steps: receiving a to-be-processed message; performing message content analysis on the to-be-processed message according to the OPC DA protocol format to obtain message analysis data; when the message analysis data hits a preset OPC DA protocol security policy matching item, determining a policy predetermined action corresponding to the to-be-processed message; when the strategy predetermined action is a blocking action, constructing an ACK response message; replying an ACK response message to a source address corresponding to the message to be processed; and recording the transmission layer bearer data length corresponding to the to-be-processed message into the diffseq field of the connection information of the current industrial gateway equipment, and discarding the to-be-processed message. According to the method, on the basis of realizing the safe filtering function of the OPC DA protocol, the TCP connection cannot be disconnected, so that the real-time performance and the reliability of the OPC DA protocol are ensured.

The invention discloses a self-adaptive range query filtering method, system and device and a storage medium. The method comprises the steps that each key in an input data set is segmented into a prefix and a suffix, the keys with the same prefix are divided into the same partition, and each partition comprises the number of the prefixes, the number of the suffixes and suffix arrays arranged in an ascending order; a self-adaptive range filter is constructed, in the construction process, coding is carried out with partitions as units, prefixes are inserted into a cuckoo filter, suffixes are inserted into a bridge structure, suffix arrays are continuously and compactly stored according to the sparse degree, and compression processing is carried out; according to the query range, the left boundary and the right boundary are segmented into two prefix-suffix pairs, the corresponding partition is rapidly positioned according to the prefix, range query is executed in the suffix array of the partition, and the suffix meeting the condition is returned. According to the invention, the self-adaptive range query filter based on fingerprints is realized through key segmentation and a specific coding mode, the inclined workload can be adaptively coped, and the query performance is improved.

The invention discloses a detectability quantitative model and a filtering method for an underwater multi-source autonomous navigation system, and belongs to an underwater robot integrated navigation and integrated positioning technology. The method comprises the following steps: firstly, respectively carrying out error modeling on SINS, DVL and USBL sensors based on an SINS / DVL / USBL integrated navigation model; establishing a multi-dimensional detectability quantitative model from four dimensions of coverage rate, accuracy rate, real-time rate and availability rate; and finally, based on the established detectability quantitative model, the detectability of the sensor is incorporated into an adaptive Kalman filtering algorithm so as to optimize the problem of computing resource allocation during data fusion. Compared with a traditional detectability method, the underwater environment interference is considered to establish a multi-dimensional detectability quantitative model, a corresponding algorithm is proposed to dynamically allocate computing resources, and the precision and robustness of the underwater vehicle multi-source autonomous navigation system are further improved.

The invention discloses an adjusting and optimizing filtering method and system for a petroleum filtering device, and relates to the technical field of petroleum filtering, and the method comprises the following steps: initially checking and establishing a petroleum feature set; and inputting the petroleum feature set into a filter screen setting model to generate a filter screen configuration result. And key adjustment parameters of the petroleum filtering device are established. Big data is called through a petroleum feature set, a fitness function is constructed, data selection is performed based on the big data and in-plant control data, and a solution space and an initial solution set are established. And evaluating the initial solution set based on a fitness function, performing iterative screening, and establishing an optimal solution identifier and an inferior solution identifier. And performing initial solution set reproduction and solution space variation through optimal solution and inferior solution identification, and executing initial solution set iteration. And generating an optimization result according to the iteration result, and performing tuning filtering treatment according to the filter screen configuration result and the optimization result. Therefore, the technical effects of improving the adaptability of the petroleum filtering device and improving the filtering efficiency are achieved.