812 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.

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 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 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 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 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 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 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 provides a three-phase grid-connected phase-locked loop two-stage filter and a filtering method thereof, and belongs to the related technical field of effective detection and extraction of power grid voltage phase signals. The invention provides a three-phase grid-connected phase-locked loop two-stage filter and a filtering method thereof in order to solve the problem that a traditional phase-locked loop filter cannot completely filter harmonic waves and direct-current offset voltage due to insufficient filtering capacity under a non-ideal power grid condition. In order to enhance the anti-interference capability of a phase-locked loop filter, the invention designs a mixed-order generalized integrator and constructs a double-mixed-order generalized integrator. Meanwhile, the double-mixed-order generalized integrator serves as a first-stage filtering unit, and a Parker transformation unit and a comb filter are introduced to serve as a second-stage filtering unit, so that a double-stage filter is formed through combination. By applying the filtering method in the phase-locked loop, it is proved that the method can effectively filter interference in a power grid. The method is mainly used for the grid-connected phase-locked loop filtering process under the non-ideal power grid condition.

The invention discloses an abnormal data real-time filtering method and system based on edge computing in a dynamic environment system, the method is executed by an edge computing gateway, sliding window weighted average preprocessing is equivalently realized by adopting integer shift operation, and the single computing overhead is controlled within 50 clock cycles; dynamically and adaptively adjusting dynamic reference model parameters based on the ratio of the network load to the sensor sampling frequency; performing multi-stage anomaly filtering of a hard threshold value, a mutation rate and a statistical interval on the data through a three-stage pipeline judgment structure executed by atomization; an FPGA hardware queue manager independent of a main processor bypasses a TCP stack to push abnormal data at the highest priority, and redundant data is stored in a zero-copy annular buffer area managed by DMA. According to the invention, the problems of intranet congestion, server I / O bottleneck and alarm delay under the centralized architecture of the traditional dynamic loop system are solved, and the fault, fault, fault, fault, fault and fault are realized on resource-limited platforms such as Cortex-M4 and the like; the average alarm delay is 8 milliseconds; and the data compression rate is more than 90%.

The invention discloses a generative information extraction method and system, and the method comprises the steps: obtaining different information extraction instructions, transmitting the information extraction instructions to a large language model, and obtaining an information extraction task instruction set; obtaining an original data set, and processing input of samples in the original data set by using a large language model to obtain a candidate sample set; scoring the text quality of the samples in the candidate sample set and the similarity of sample input in the original data set to obtain sample input; filtering a noise sample in the sample input by using a first noise filtering method to obtain a first sample, and converting structured text data of the first sample to obtain a second sample; and adjusting the large language model by combining the second sample with the information extraction task instruction set to obtain an adjusted large language model so as to realize information extraction. Good adaptability is shown in different fields and tasks.

The present invention relates to the field of real-time data filtering technology, and in particular to a real-time data filtering method and system with multi-dimensional feature fusion. The method comprises the following steps: obtaining data transmission logs and extracting original transmission data features, parsing data frame field structure information; identifying multi-dimensional abnormal coupling of data transmission based on field time gradient change rate and dynamic offset status; further detecting abnormal dilution of transmission data, predicting the data transmission complexity exceeding limit state, and detecting the overload condition of data filtering chip structure; then evaluating the attenuation trend of data real-time filtering efficiency, and optimizing the data real-time filtering path accordingly, completing real-time filtering processing of data transmission complexity exceeding limit condition, and outputting filtering results; the present invention achieves higher credibility of data filtering by filtering data in real time.

The invention provides a filtering method and device for channel estimation, electronic equipment and a storage medium. The method comprises the following steps: acquiring initial channel estimation of a reference signal, and determining optimal noise power in a current target channel scene based on the initial channel estimation; performing first-stage Wiener filtering on the initial channel estimation to obtain first filtering channel estimation; and performing second-stage Wiener filtering on the first filtering channel estimation based on the optimal noise power to obtain second filtering channel estimation. Therefore, according to the scheme, the optimal noise power in the target channel scene is acquired in advance, so that the second-stage Wiener filtering can be immediately carried out after the first-stage Wiener filtering is completed, the process of carrying out noise estimation on the reference signal after the first-stage filtering can be omitted, the processing waiting time is reduced, and the processing efficiency is improved. And the processing time delay is reduced, so that the two-stage filter circuit can be designed in a tight coupling manner to enhance the stability of the filter performance.

The invention provides a low-altitude target monitoring, positioning and tracking method and system based on a civil network, and the method comprises the steps: enabling a plurality of terminal devices at different positions to point to and align at a low-altitude target, collecting target measurement data, and uploading the target measurement data to a background server through the civil network; performing abnormal point detection and elimination by adopting a Z-score detection and dynamic time window filtering method, judging a target batch and a target type according to uploaded data, states and time intervals, and storing the target batch and the target type in a database in an original data form; performing fusion processing on a plurality of pieces of uploaded data through a trajectory fusion algorithm to form a target trajectory, and monitoring, positioning and tracking a low-altitude target; and the target track is sent to a front-end server for target fusion result display, and track information of all the low-altitude targets is completely displayed. According to the invention, data are collected through the terminal equipment, low-altitude targets are monitored, positioned and tracked based on a civil network, low-altitude, slow and small targets are detected and found in time, the detection efficiency is improved, and the system equipment cost is reduced.

A traffic filtering method includes a network edge node that provides a cloud service that receives target traffic; obtains a filtering rule, where the filtering rule is for filtering, based on a filtering action, target traffic that meets a filtering condition; invokes a rule engine to parse and execute the filtering rule, where the rule engine is deployed in the network edge node or in a network edge processing system connected to the network edge node; and obtains filtered target traffic based on an execution result, where the filtered target traffic is traffic that meets a filtering requirement corresponding to the filtering rule. The target traffic filtered according to the filtering rule includes at least one of traffic sent by a network side to a user terminal and traffic sent by the user terminal to the network side.

The invention discloses a method for identifying interference signals of weld defects under different lifts-off conditions, which comprises the following steps of: S1, de-trending processing: carrying out de-trending processing on original detection signals; s2, Gaussian wavelet transform: carrying out Gaussian wavelet transform on the detrended signal; s3, optimal wavelet basis selection: by calculating correlation coefficients or energy ratios of different wavelet basis and defect signals, selecting the wavelet basis with the highest matching degree for reconstruction; s4, envelope processing: extracting a signal envelope based on Hilbert transform; s5, mean filtering: applying sliding window mean filtering to the envelope signal; and S6, threshold processing: setting a self-adaptive threshold screening signal, and retaining the feature points of which the amplitudes exceed the threshold. According to the method, de-trending and Gaussian wavelet transform are used for de-noising enhancement. According to the method, wavelet functions of different orders are constructed and matched with defects, secondary signal enhancement is carried out by selecting a filtering method, finally, threshold stripping interference is calculated, reliable defect identification is carried out, and technical support is provided for uneven welding seam quality monitoring.

The invention discloses a big data real-time calculation abnormal value filtering method and a terminal. The method comprises the following steps: acquiring scene basic data, establishing a relevance rule of master data and slave data in the scene basic data, determining upper and lower limit parameters of the master data, the slave data and the scene basic data according to data attributes, and calculating a basic data value of the scene basic data; determining a preset range of the scene basic data by using machine learning in combination with upper and lower limit parameters of the master and slave data and the scene basic data; if the basic data value of the scene basic data is within the preset range, calculating the fluctuation range and the fluctuation value, and if the fluctuation value exceeds the fluctuation range, determining that the scene is abnormal; if the fluctuation value does not exceed the limit, the deviation proportion is calculated, and if the deviation proportion exceeds a preset abnormity judgment coefficient, abnormity is judged; if the deviation proportion does not exceed the limit, finally judging the deviation proportion through an abnormal rule in a preset decision tree; abnormal data are eliminated, filtered data and basic data values thereof are obtained, and the adaptability and reliability of abnormal value detection are improved.

The invention relates to the technical field of point cloud data processing, and provides a ground filtering method and system based on cloth simulation parameter dynamic optimization, and the method comprises the following steps: obtaining point cloud data of a to-be-processed region, carrying out the preprocessing, constructing a complexity index, and calculating a complexity index value based on the preprocessed data; clustering the obtained complexity indexes by adopting a clustering method fusing an elbow rule and a spatial neighborhood constraint to obtain a terrain complexity category; and selecting distribution parameters of a distribution algorithm based on the terrain complexity category and the dynamic mapping of the constructed complexity category-optimal parameter group, and further performing ground point filtering and ground extraction by adopting the distribution algorithm. According to the invention, distribution simulation parameter configuration is guided through terrain complexity analysis, accurate adaptation of different terrain areas is realized, and the accuracy and global consistency of ground point extraction are improved.