64 results about "Entropy model" patented technology

The invention relates to the technical field of industrial Internet of Things data processing, in particular to a dual-channel anomaly detection method based on local entropy and isolated forest, and the method comprises the steps: collecting multi-dimensional time sequence data of equipment operation in real time; extracting local data of the multi-dimensional time series data in each sliding window by using a variable step size sliding window technology, calculating four statistical indexes according to the local data to construct a multi-dimensional entropy model, and obtaining a local entropy score according to the multi-dimensional entropy model; utilizing an isolated forest algorithm to calculate an isolated forest abnormal score of the multi-dimensional time series data; and constructing a joint scoring model, dynamically adjusting the weight of the local entropy score and the weight of the isolated forest abnormal score to obtain a joint score, and judging the abnormal level of the equipment according to the joint score and a preset abnormal threshold value. Therefore, the problems of insufficient real-time performance, insufficient feature utilization, low decision collaboration efficiency and the like in an industrial scene are solved.

Methods and systems implement a pleno-generation face video compression framework with bandwidth intelligence for generative models and compression. Heterogeneous-granularity facial description regularizes long-term dependencies between video frames and compensates for motion estimation errors caused by compact representations of motion information. A generative decoder reconstructs heterogeneous-granularity visual representations, providing auxiliary visual signals for attention-based recalibration of a GFVC-reconstructed face signal. A coarse-to-fine generation strategy avoids error accumulation. High efficiency for heterogeneous-granularity signal compression is achieved by two different entropy-based signal compression methods: heterogeneous-granularities feature representation from the key-reference frame as hyperpriors to optimize the entropy model for compressing heterogeneous-granularity feature from subsequent inter frames, and a feature difference operation for heterogeneous-granularities feature representation between key-reference and subsequent inter frames, such that the entropy model only compresses heterogeneous-granularities feature residual for redundancy reduction. Mixed-model dataset generation and training and model-specific dataset generation and training are also provided.

The invention provides a compressor intelligent control method and system based on dynamic pressure relief and a storage medium, and the method comprises the steps: firstly, capturing the multi-physical field coupling characteristics of a compressor in a shutdown state in real time by deploying a pressure, temperature, vibration and current four-dimensional sensing network; secondly, based on a preset pressure entropy model, fusing the pressure fluctuation intensity, the temperature anomaly index, the high-frequency vibration factor and the current attenuation coefficient to quantitatively evaluate a pressure entropy value, and triggering a three-stage pressure relief mode based on the pressure entropy value; then, based on the deviation between the cavity pressure and the target pressure, pressure relief parameters are dynamically adjusted, and stable pressure transition is achieved; and finally, collecting full-link data of pressure relief operation, and dynamically optimizing pressure entropy model parameters based on reinforcement learning, so that the system continuously adapts to equipment aging and working condition changes. Through dynamic closed-loop adjustment, pressure relief energy consumption waste and equipment loss are reduced, and the stability of the pressure relief process is improved.

This invention discloses a method, device, and product for hyperspectral remote sensing image compression based on a hybrid hyper-prior. This method utilizes a CNN- and MLP-based backbone analysis network and synthesis network to transform the original image into a compact latent representation, obtaining superior latent representation features. Furthermore, a spatial-pass-through attention module is used to further enhance the representational capabilities of the CNN-MLP hybrid network module, enabling in-depth exploration of redundant information within hyperspectral images. When constructing the entropy model, a CNN-based hyper-prior is used to mine local redundant information, while a Transformer-based hyper-prior is used to extract non-local redundant information. The two are then combined to predict the parameters of the entropy model, improving the accuracy of the entropy model construction and, consequently, the final image compression performance.

The invention discloses a Mama-based entropy model and an image compression method, and mainly solves the problem of limited compression performance caused by poor entropy estimation precision in the prior art. The scheme comprises the following steps: 1) constructing a two-dimensional state space hyper-prior network by a hyper-prior encoder and a decoder to obtain global hyper-prior features; 2) adopting a double-branch gating architecture to obtain a mixed context feature containing local and global dependency information; 3) constructing an entropy parameter fusion and probability modeling network, carrying out channel splicing and feature fusion on the mixed context features and global super-prior features, and outputting a conditional Gaussian distribution parameter of each latent variable position; according to the method, the entropy model estimation precision can be remarkably improved, meanwhile, the compression rate distortion performance is improved, and the balance between the rate distortion performance and the calculation complexity is achieved.

The invention discloses a photovoltaic cleaning robot path planning method fusing string level power generation analysis, and belongs to the technical field of data processing, and the method specifically comprises the steps: dividing historical string power generation data according to the same interval to construct an entropy model, collecting the data in real time, calculating an interval entropy, comparing the interval entropy with the model, and recognizing a suspected low power generation interval; key cleaning group strings are screened in combination with spatial neighborhood information, and a posterior probability is calculated through a Bayesian probability model to determine a cleaning priority; power station layout constraints and robot motion characteristics are combined, and an optimal cleaning path is generated by adopting a path planning algorithm; through multi-dimensional data fusion and an intelligent algorithm, precise identification and path optimization of photovoltaic string cleaning requirements are realized, the cleaning efficiency is effectively improved, and the operation and maintenance cost is reduced.

The invention relates to a man-machine mixed vision-oriented semantic perception video compression method and system, and the method comprises the steps: extracting the dynamic semantics of a video sequence, and generating a region of interest; generating a focusing frame with consistent vision according to the input frame and the corresponding interested area mask; predicting feature probability distribution of the focusing frame through an entropy model, and compressing the feature probability distribution into a code stream; decoding the code stream through a conditional decoder to obtain a semantic compressed reconstructed video; performing feature alignment on the decoded frames in the decoded frame buffer areas of the basic branch and the auxiliary branch to generate prediction features; inputting the prediction frame of the prediction feature and the video sequence into an entropy model, and compressing the prediction frame and the video sequence into a code stream through entropy coding; decoding the code stream to obtain reconstruction features; and converting the reconstruction features into fine reconstruction features to obtain a final compressed and reconstructed video. Compared with the prior art, the method has the advantages that high machine vision task accuracy can still be maintained under the condition of low code rate, and higher rate accuracy performance is achieved in the machine vision task.

The invention discloses a remote sensing image compression method and device based on frequency domain enhancement and adaptive optimization. The method comprises the following steps: compressing and decompressing a remote sensing image through a pre-training first model deployed at a compression end and a decompression end at the same time; the first model comprises an encoder, an entropy model and a decoder; the encoder and the decoder perform frequency domain analysis on input through a frequency domain enhanced selective state space module, and model a long-distance dependence and frequency domain structure based on a state space mechanism; training the first model according to a preset training set and the loss function to obtain a pre-trained first model; compression includes: extracting, by an encoder, a main feature of an input image; probability modeling is carried out on the distribution information of the main features through an entropy model, a bit stream is generated, and the bit stream is sent to a decompression end; the decompression comprises the following steps: a decompression end receives a bit stream; restoring the bit stream into a main feature through an entropy model; and inputting the main features into a decoder to obtain a remodeled image.

The invention discloses a video compression method based on deep learning, particularly relates to the field of video compression, is used for solving the problem that dynamic migration of residual distribution cannot be tracked in real time by entropy modeling in deep learning video compression, and accurately depicts complex distribution of residual in space and time dimensions through a multi-scale self-attention mechanism. The adaptive entropy model configuration on each piece of data is realized by using a content-based mixed density generator, a bidirectional evaluation quantity is sent to a pre-training attention model to dynamically generate a threshold value to drive a high-precision coding template to implement fine compression on a high-frequency region, and meanwhile, entropy parameters are locally contracted based on real-time bit feedback to balance output. And the consistency of a decoding end model is guaranteed through parameter freezing synchronous metadata, so that synchronous optimization of compression efficiency and reconstruction quality is realized, fluctuation is converged, artifact deviation is suppressed, and the distortion limit is approached.

The invention relates to a three-dimensional point cloud prediction geometric coding method based on deep learning, and the method comprises the steps: 1, forming a prediction tree through points collected by each laser transmitter, and converting an original laser radar point cloud LPC into a plurality of prediction trees for representation; 2, respectively designing different predictors and entropy encoders for each component of the coordinates of the points, and compressing each component of the coordinates of the points by applying different quantization step lengths; 3, selecting a quantization step size for quantifying each component by adopting a quantization step size selection strategy; 4, adopting an entropy model to model the probability distribution of the residual error of each component so as to encode the residual error entropy; and step 5, decoding is realized through a reverse process from the step 1 to the step 4. Compared with other methods, the method provided by the invention obtains the best rate distortion performance.

The present invention relates to a machine vision encoding method and system based on self-supervised learning. The method includes the following steps: randomly sampling image information into sub-blocks, inputting the sub-blocks into a backbone network head to extract and transform feature channels to obtain a first feature; transforming the first feature to obtain a feature in a low-dimensional space, adding uniform noise to the feature in the low-dimensional space through a quantizer to obtain a quantized feature, and reconstructing a compressed feature to obtain a second feature; transforming the second feature to a low-dimensional space, adding uniform noise to the feature in the low-dimensional space through a quantizer to reduce redundancy, extracting and encoding side information, decoding the side information, using a mixed Gaussian entropy model to predict the probability distribution parameters and bit rate of the second feature, and reconstructing the dimension of the encoded feature as a third feature; extracting and transforming the dimension of the third feature, extracting and weighting the convolution feature to form a heat map, obtaining valid positive samples through the heat map, and obtaining an encoding result. Compared with the existing technology, the present invention has the advantages of low encoding complexity and high semantic reliability.

The invention relates to the technical field of education artificial intelligence, and provides a self-adaptive learning path recommendation method for artificial intelligence general recognition education, which comprises the following steps of: 1, calling a large language model to analyze unstructured course resources of AI general recognition education, mining semantic association and implicit dependency of knowledge points, and constructing a learning path; generating a space-time knowledge graph containing confidence scores, automatically capturing the latest literature and tool update of the field every 72 hours, and iteratively optimizing the topological structure of the graph; 2, deploying data acquisition points to acquire multi-source learning data of a user in real time, and calculating a dynamic cognitive state vector with knowledge points as dimensions by mastering an entropy model; the knowledge graph is automatically iterated through LLM, and the problem of update lag is solved; precise personalized recommendation is realized according to multi-dimensional data and a dynamic algorithm, and cognitive differences are adapted; visually presenting decision logic, and cracking a decision black box; the method can be migrated to multiple fields, supports multi-terminal and offline learning, exceeds an expected adaptive scene, and improves the learning efficiency and credibility.

An entropy-based technique is used to select a large language model capable of generating fluent natural language text. An entropy model, trained on fluent natural language samples, is used to determine the entropy of a large language model based on an output text generated by the large language model. The entropy of a machine-generated natural language text is used to quantify the amount of information that the large language model holds with respect to the tokens and context of an input text segment. The entropy score of a model is then used to select a large language model capable of generating fluent text or to select the most fluent machine-generated output text produced by a set of large language models.

The invention discloses a full-stream entropy encoding and decoding accelerator, which comprises an encoder and a decoder, the encoder comprises an encoding prefetching module and an encoding pipeline calculation core, the decoder comprises a decoding prefetching module and a decoding pipeline calculation core, the encoding prefetching module is used for reading information of a plurality of sets of entropy models at a time, and the decoding pipeline calculation core is used for decoding the encoding pipeline calculation core. Determining that the coding process and the decoding process of the to-be-coded characteristic value are not skipped according to the confidence coefficient, splitting the multiple sets of entropy model information into single sets of entropy model information in a parallel-to-serial mode, and inputting the single sets of entropy model information into a coding pipeline calculation kernel in combination with the corresponding to-be-coded characteristic value for coding to obtain a code stream; the plurality of decoding prefetching modules are used for polling and inputting the multiple segments of code streams and the corresponding entropy model information into the decoding pipeline calculation core for decoding, the decoding pipeline calculation core is used for intercepting the code streams corresponding to the reserved digits, calculating the anti-cumulative probability distribution value of the code streams, carrying out quantization processing on the anti-cumulative probability distribution value to obtain decoded characteristic values, and sending the decoded characteristic values to the decoding prefetching modules; the problems of computing resource waste and low storage efficiency are solved.

The application provides a digital semantic perception OFDM subcarrier allocation method based on an entropy model, and relates to the field of wireless communication. The method comprises the following steps: a semantic encoder in a joint coding and modulation module is used to perform semantic coding on an original image to obtain semantic features; the joint coding and modulation module is used to modulate the semantic features to obtain a modulation symbol sequence; a semantic importance evaluation module is used to process the semantic features to obtain a semantic importance vector; an orthogonal frequency division multiplexing transmitter is used to process the modulation symbol sequence into M to-be-transmitted orthogonal frequency division multiplexing data packets; the joint coding and modulation module is used to allocate the M to-be-transmitted orthogonal frequency division multiplexing data packets to M subcarriers according to the semantic importance vector and feedback channel state information; and the orthogonal frequency division multiplexing transmitter is used to send the M to-be-transmitted orthogonal frequency division multiplexing data packets to an orthogonal frequency division multiplexing receiver in a receiving end, so as to realize efficient and reliable transmission of digital semantic information in a wireless channel.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for compressing and decompressing data. In one aspect, a method comprises: processing data using an encoder neural network to generate a latent representation of the data; processing the latent representation of the data using a hyper-encoder neural network to generate a latent representation of an entropy model; generating an entropy encoded representation of the latent representation of the entropy model; generating an entropy encoded representation of the latent representation of the data using the latent representation of the entropy model; and determining a compressed representation of the data from the entropy encoded representations of: (i) the latent representation of the data and (ii) the latent representation of the entropy model used to entropy encode the latent representation of the data.

An entropy-based technique is used to select a large language model capable of generating fluent natural language text. An entropy model, trained on fluent natural language samples, is used to determine the entropy of a large language model based on an output text generated by the large language model. The entropy of a machine-generated natural language text is used to quantify the amount of information that the large language model holds with respect to the tokens and context of an input text segment. The entropy score of a model is then used to select a large language model capable of generating fluent text or to select the most fluent machine-generated output text produced by a set of large language models.

An apparatus includes first and second circuitry, such as a first memory and a second memory. The first circuitry is configured to store compressed information representing a set of distribution functions associated with a set of entropy models. The second circuitry is configured to store mappings of requests for subsets of the set of distribution functions to one or more offsets in the first circuitry corresponding to the compressed information representing the subsets. The apparatus also includes circuitry that implements the set of entropy models. This circuitry is configured to perform entropy coding based on the subsets and a first entropy model selected from the set of entropy models. This circuitry can include one or more entropy engines that have a set of ports for encoding or decoding one or more streams of bits or symbols. Multiple streams can therefore be encoded or decoded concurrently or in parallel.

The invention discloses a long tribulus terrestris risk prediction method based on a multi-source data fusion maximum entropy model, and belongs to the technical field of long tribulus terrestris risk prediction. The objective of the invention is to solve the problem of accurate risk prediction of tribulus terrestris. The method comprises the following steps: collecting species distribution data and environment variable data of tribulus terrestris, and preprocessing; constructing a maximum entropy model; a three-level screening strategy is adopted, key environment variable screening is carried out on the preprocessed environment variable data in combination with the constructed maximum entropy model, and key link variables after screening are obtained; training a maximum entropy model by using the preprocessed species distribution data and the screened key link variables to obtain a trained maximum entropy model, and outputting a adaptability index of a grid unit; and carrying out potential suitable growth area division and early warning area delimitation on the tribulus terrestris. According to the invention, the space-time accuracy and prevention and control guidance value of the early warning result are greatly improved.

The invention discloses a three-dimensional point cloud geometric compression method and device based on a diffusion model and a medium, and the method comprises the steps: enabling an input point cloud to pass through a dual-domain encoder, obtaining low-frequency features and high-frequency features, and extracting skeleton points for space guidance; probability distribution of the low-frequency features is modeled through a full decomposition entropy model, probability distribution of the high-frequency features is modeled under low-frequency guidance, arithmetic coding is carried out, and skeleton points are subjected to lossless compression through G-PCC to jointly form a final compressed code stream; and carrying out multi-round iterative denoising reconstruction through a diffusion model, taking low-frequency features, high-frequency features and skeleton points as conditional information, inputting the conditional information into a decoding process in a mode of affine transformation injection and attention mechanism fusion, and guiding gradual recovery of a point cloud structure and details. According to the method, more robust point cloud geometric feature extraction is realized through a high and low frequency feature double-path fusion mode, a diffusion reconstruction process based on affine transformation and an attention mechanism is further guided, and the compression efficiency and the reconstruction quality are effectively improved.

The invention relates to a screen content image compression method and system based on frequency perception feature extraction and entropy model construction. Comprising the following steps: analyzing transformation frequency division: inputting an original screen content image to obtain multi-frequency potential features; frequency perception entropy model and quantization: inputting multi-frequency potential features to obtain quantized multi-frequency potential features, and performing entropy coding and entropy decoding; performing hyper-prior analysis conversion frequency division: inputting the multi-frequency potential features to obtain hyper-prior multi-frequency potential features; quantization and entropy coding: inputting the hyper-priori multi-frequency potential features to obtain quantized hyper-priori multi-frequency potential features, and performing entropy coding and entropy decoding; super-prior synthesis transformation frequency division: quantized super-prior multi-frequency potential features obtained after entropy decoding are input; and synthesizing, transforming and dividing frequency: inputting the quantized multi-frequency potential features obtained after entropy decoding to obtain a final reconstructed screen content image. The method provided by the invention has better performance on a public data set.

The invention provides a dynamic grid animation coding method, a decoding method, a coding device and a decoding device based on a depth entropy model.The dynamic grid animation coding method comprises the steps that A1, vertex position offset of a grid animation frame sequence is coded, and a prediction model is constructed through vertex position information of a preorder frame; a2, based on the vertex position information of the preorder frame, dividing the vertex into a base layer and an inference layer by adopting hierarchical detail layering and uniform sampling layering; a3, for the vertexes in each layer, using inverse distance weighted interpolation based on nearest neighbors to predict the offset of the vertexes, and calculating a prediction residual error; a4, carrying out the coding of the prediction residual error of the vertex of the base layer through run-length coding; and coding the inference layer vertex by using a depth attention model based on an adaptive position descriptor. According to the method disclosed by the invention, the vertex position offset of two adjacent frames of animation sequences is coded in combination with a coding framework of deep learning, so that the compression performance of the grid animation is improved.

The invention discloses a privacy-enhanced image compression method, which comprises the following steps: constructing a semantic-frequency map based on frequency domain features and sensitive masks, and positioning a semantic unit set; performing spectral domain structure adjustment on the frequency domain features, performing entropy alignment generation in combination with an entropy model, and obtaining adjusted frequency domain features, parameterizing semantic differences and dual compensation; reversible dual decomposition is applied to the adjusted frequency domain features, and public potential features and dual potential features are obtained; an entropy model is utilized to compile the public potential feature entropy into a main bit stream, and the dual potential feature, the parameterized semantic difference and the dual compensation joint entropy are compiled into a safe bit stream; and generating a protected head containing access control information in combination with the access key, and packaging the main bit stream, the security bit stream and the protected head into a container code stream. According to the method, machine semantic recognition can be effectively shielded at a common end, meanwhile, an authorization end is supported to accurately recover semantics at low code rate cost, and multi-level access control of the same code stream is achieved.

The invention relates to a presentation strategy evaluation and optimization method based on a perception-judgment-decision link, and solves the problems that in the existing intelligent auxiliary information situation interface design, the cognitive load is difficult to quantify, the task completion time evaluation is inaccurate, and the causal logic understanding lacks scientific indexes. According to the method, by constructing a multi-dimensional information entropy model, a task completion timeliness model and a causal definition model, the interface cognitive load, the task operation efficiency and the understanding degree of a user on task logic are quantified, then an optimal interface design scheme is screened through a multi-objective optimization method, the operation efficiency and the situation understanding ability of the user are improved, and the user experience is improved. And the information presentation effect of the intelligent auxiliary system is optimized. The method provided by the invention can effectively reduce cognitive load, shorten task completion time, enhance understanding of users on task causal relationships, and significantly improve usability and security of an intelligent auxiliary system interface, and has wide application value.

The application provides a point cloud encoding method based on a multi-level spherical octree and a graph-driven attention entropy model, comprising: encoding point cloud data using an octree entropy model based on multi-level spherical coordinates; constructing an adjacency matrix of a parent graph and a distance graph; using a graph convolution network module to embed context information with the aid of the adjacency matrix; constructing a grouping graph attention module and a cross attention module to learn the relevance of parent node context and sibling node context; predicting the probability of each octree node placeholder symbol; compressing the placeholder symbol sequence into a binary floating point number sequence and converting it into a bit stream; converting the bit stream into an octree placeholder symbol sequence, reconstructing the octree and restoring the point cloud. The application combines multi-level spherical coordinate octree structure, graph convolution, grouping graph attention module and cross attention module, reduces quantization error, improves compression efficiency, balances calculation complexity and compression effect, and significantly enhances the adaptability to high-resolution point cloud data.

An apparatus includes first and second circuitry, such as a first memory and a second memory. The first circuitry is configured to store compressed information representing a set of distribution functions associated with a set of entropy models. The second circuitry is configured to store mappings of requests for subsets of the set of distribution functions to one or more offsets in the first circuitry corresponding to the compressed information representing the subsets. The apparatus also includes circuitry that implements the set of entropy models. This circuitry is configured to perform entropy coding based on the subsets and a first entropy model selected from the set of entropy models. This circuitry can include one or more entropy engines that have a set of ports for encoding or decoding one or more streams of bits or symbols. Multiple streams can therefore be encoded or decoded concurrently or in parallel.

The invention discloses an extremely low bit rate image compression method based on mixed attention and multi-scale entropy modeling, and the method comprises the steps: obtaining a standard image data set which comprises a training set and a test set; the method comprises the following steps: constructing an extremely low bit rate image compression network model based on double-branch mixed attention and multi-scale entropy modeling, constructing a loss function of an extremely low bit rate image compression network, and in a deployment stage, inputting an original image in a test set into the extremely low bit rate image compression network loaded with an optimal weight, and obtaining a coded and compressed binary code stream and a decoded and reconstructed image. According to the method, a multi-scale attention fusion mechanism is introduced into an autoregression entropy model, the problem of dimensionality fragmentation of feature representation is solved through three parallel branches, and cross-dimensional interaction, local spatial correlation and grouping channel correlation are modeled to jointly enhance context features. Therefore, potential space redundancy in the compression process is reduced.

The invention provides an image emotion mark distribution learning method and system based on metric learning. The method comprises the following steps: acquiring an image emotion data set, and preprocessing data to extract image features; constructing a triple set based on the marked image emotion data; mapping the image features to a low-dimensional embedding space by using a metric learning technology; constructing a marker distribution prediction model by adopting a maximum entropy model, and optimizing embedded features by minimizing an objective function to realize optimal marker distribution prediction; wherein the objective function combines the KL divergence loss between the real marker distribution and the predicted marker distribution, and the triple ratio distance loss of the relative distance relationship between the samples is measured by the ratio relationship. According to the method, the relation between marks is modeled through the triple ratio distance loss function, mark correlation can be implicitly learned, feature selection can be optimized, the training model can more accurately utilize feature and mark information, and therefore the accuracy and robustness of image emotion mark distribution prediction are improved.

The present invention proposes a method and system for estimating the highest order of side channel moment information. The present invention calculates the data distribution frequency of each subinterval and the midpoint value of the subinterval; calculates the origin moment of each order in combination with the midpoint value of each subinterval, and calculates the power consumption value matrix of each order; solves by the conjugate gradient method to obtain the parameter weight vector in the maximum entropy model after solution, calculates the entropy value of each subinterval at each order, and further calculates the maximum entropy value at each order; calculates the difference between the maximum entropy values ​​of two adjacent orders, and calculates the absolute value of the difference between the maximum entropy values ​​of two consecutive groups of adjacent orders. If the absolute value of the difference between the maximum entropy values ​​of two consecutive groups of adjacent orders is less than a threshold, the corresponding order is used as the highest order after solution; the highest order after solution is further used to set the highest value of the moment information order used in the side channel leakage assessment, attack, and modeling process, reducing unnecessary consumption of computing resources for higher-order information.

The application discloses a kind of based on physical event anchoring and space-time decoupling's sea transport freight right fraud real-time blocking system, comprising: physical event four-dimensional tensor construction module: for building the physical event including space coordinates, time series, equipment signature and event type four-dimensional data tensor representation;Quantum fingerprint anchoring module: for generating anti-copy unique identification to physical event data by 12-bit quantum circuit;Helve space-time topology engine: for calculating the continuity score of the physical event of generating anti-copy unique identification;Three-level risk response execution module: for automatically triggering on-chain blocking operation according to physical event continuity score, three-level risk response is judged;Self-evolution risk control system: for dynamically optimizing rule weight and freight right entropy model by fraud mode clustering, generates the defense mechanism of triggering on-chain blocking operation.The system marks that international trade trust mechanism has entered the trend and possibility of new era of quantum level credibility.