46 results about "Dynamic channel" patented technology

The invention relates to the technical field of data fusion, in particular to a multi-source electrocardiosignal correction method and system based on adaptive fusion, and the method comprises the following steps: constructing a multi-channel input tensor, extracting local features through a weight calculation network, carrying out the adaptive weight fusion and dimension reduction of multiple paths of signals, and carrying out the correction of the multi-source electrocardiosignal. A nonlinear mapping relation is established through a deep reconstruction network, a standard waveform is reconstructed, and network parameters are optimized based on reconstruction error reverse iteration. According to the method, local neighborhood features of multichannel signals are extracted by constructing a weight calculation network, a dynamic channel weight sequence reflecting the real-time contribution degree of a signal source is constructed, the amplitude intensity is adaptively adjusted according to the signal quality, unstable channel noise interference is effectively inhibited, and high-quality signal components are enhanced; a deep reconstruction network is used for carrying out nonlinear feature transformation on a fusion sequence, accurate mapping from non-standard input to standard lead waveforms is established, and weight distribution and optimization of signal reconstruction parameters are achieved in combination with an error back propagation mechanism.

The invention provides an eddy current sensor signal noise reduction method based on frequency division detection low-pass filtering and a monitoring system, belongs to the technical field of sensor signal processing, and can at least partially solve the problems of unstable signals and inaccurate measurement in the prior art. The signal noise reduction method for the eddy current sensor comprises the following steps: processing an original output signal of the eddy current sensor by adopting a synchronous detection module, demodulating a dynamic vibration alternating current component of the original output signal into a direct current voltage signal by utilizing a reference signal, and separating a static displacement direct current component signal; an independent dynamic signal channel and an independent static signal channel are formed; filtering high-frequency noise in the dynamic signal channel through a dynamic channel low-pass filter with a first preset cut-off frequency, and filtering power frequency interference in the static signal channel through a static channel low-pass filter with a second cut-off frequency; and reversely injecting compensation voltage at the output end of the static channel low-pass filter so as to eliminate the direct-current offset error of the static displacement component signal.

The invention discloses a double-flow fault diagnosis method based on state space modeling and dynamic edge context awareness, the method constructs a time domain state flow and a time frequency edge flow, and the specific implementation process is as follows: the time domain state flow is subjected to state space modeling under a Transform framework to form a MambaT encoder, long and short term dependence is efficiently captured with linear complexity, and the time domain state flow is subjected to dynamic edge context awareness; time domain sensitive features are accurately represented; the time-frequency edge stream converts vibration data into a two-dimensional time-frequency image through wavelet transformation, a differentiable edge sensitive mask is designed to adaptively separate time-frequency image impact characteristics and background noise, boundary content perception adaptive filling is designed to maintain image boundary topology integrity, and the two parts cooperate to form dynamic edge context perception convolution, so that the image boundary topology integrity is improved. Time-frequency edge features are effectively extracted; and a channel enhancement and channel gating module is designed to establish a dynamic channel interaction gating mechanism, so that time domain-time frequency domain double-current cascade characterization is realized, and the fault discrimination capability is remarkably improved. Experiments prove that the method has higher fault diagnosis performance.

The invention discloses a model generalization ability optimization method and system based on hybrid experts, and aims to solve the problems of weak generalization ability, difficulty in knowledge migration, low semantic alignment efficiency and the like when a multi-modal large model performs fine adjustment on downstream tasks in a new field. The method comprises the following steps: embedding a hybrid expert module in a low-rank adaptive fine tuning framework of a self-attention module in the visual encoder, introducing a heterogeneous hybrid convolution structure, innovatively realizing transverse and longitudinal independent scaling through bilinear interpolation in the convolution structure, supporting a multi-scale convolution kernel, and realizing dynamic combination through an MoE framework; constructing a cross-modal expert adapter of channel perception, and adopting dynamic channel recombination, cross-modal conditional convolution and weighted spatial feature aggregation methods; according to the method, the problem of local semantic confusion can be well solved, meanwhile, semantic fusion of image features and text features is enhanced, more effective spatial information and priori knowledge are injected into the model, and therefore the generalization of the model is improved.

The invention provides a multi-branch sequence recommendation method based on dynamic channel fusion. The method aims at solving the problems that in sequence recommendation, data have a large amount of noise and are sparse, and existing model prediction is smooth. The method comprises the following steps: on one hand, enabling a user sequence to pass through an embedding layer, introducing position information by utilizing RoPE rotation position coding, and extracting long-term dependency of a user in the sequence through a multi-feature channel feature network to obtain a new embedded Eglobal, a short-term interest and long-term dependency interactive embedded Ecross, an embedded Eema after exponential smoothing filtering and an embedded Efreq after frequency domain modulation; and on the other hand, in order to fully fuse the characteristics of different channels, a traditional hyper-parameter fusion mode is abandoned, and a pooling network layered adaptive fusion mode is adopted. Firstly, four channel features are divided into two groups including a self-attention group and a trend group, features of different channels are learned through Squeeze average pooling, learned parameters are activated through Excitation by adopting sigmoid to obtain channel weights, and feature representations of different groups are sent into a gating network for weighted summation loss calculation.

The invention discloses a back door defense method and system based on adaptive feature blocking, and the method comprises the steps: 1, constructing a blocking module, and embedding the blocking module into an image classification model; the blocking module comprises a self-adaptive instance statistical calibration layer and a dynamic channel suppression layer, the self-adaptive instance statistical calibration layer is used for eliminating cross-sample statistical offset caused by backdoor attack, and the dynamic channel suppression layer is used for suppressing abnormal activation of a polluted channel; and 2, training the embedded classification network to obtain a final classification network, and completing backdoor attack defense according to the final classification network. Through lightweight modular design and a fine adjustment mechanism, the bottleneck problem of a traditional model defense method in the aspects of calculation cost and flexibility is effectively broken through.

The invention discloses an SV message dynamic channel real-time mapping device based on a modification instruction set, comprising a configuration computer which generates a data packet corresponding to the SV modification instruction set and a data packet corresponding to SV message static data, and the message modification module modifies SV message dynamic data in a to-be-matched SV message received by the message receiving module from the to-be-matched source device based on the SV modification instruction set, then generates a modified SV message in combination with the SV message static data, and sends the modified SV message to the target device. The invention further discloses an SV message dynamic channel real-time mapping method based on the modification instruction set, and the to-be-matched SV message from the to-be-matched source device is modified and converted into the message which can be received by the target device in real time. The problems that cross-interval equipment stops for multiple times and SV messages of new and old devices are not matched in the transformation process of the secondary equipment of the intelligent substation are solved, and new equipment debugging, synchronous operation of the old equipment and seamless transformation switching of the new and old equipment are achieved.

The invention discloses a cigarette carton cache channel tide type flexible control scheduling method and system, and belongs to the technical field of cigarette carton channel control scheduling. A part of cache channels are used as flexible dynamic channels, and a system takes over brand allocation decisions of the part of channels; carrying out wave treatment on the replenishment demand; in the wave time, according to the demand quantity of replenishment of the channel type cache system for the sorting line, the number of cache channels matched with the demand is dynamically distributed for each brand; the warehouse-in and warehouse-out processes of the cache channel are managed, and warehouse-in locking is carried out on the dynamic channel in good time so as to ensure that the inventory of the dynamic channel is emptied when the replenishment wave is finished, automatic recovery of the channel is realized, the brand of the channel is redistributed for the subsequent replenishment wave, and the configuration of the cache channel of the brand and the tide-type following change of the replenishment demand are realized on the whole. After the method is applied to control scheduling of the channel type cache, the resource utilization rate of the cache channel can be improved, human intervention is reduced, and the workload and management difficulty of manual control are reduced.

The invention discloses a dynamic channel normalization method based on shift operation, and belongs to the technical field of artificial intelligence and integrated circuit design. According to the method, a division-free and floating-point-exponent-free normalization operator is constructed through piecewise linear fitting and a binarization power interpolation mechanism, so that hardware-friendly dynamic channel normalization based on shift operation is realized, traditional high-complexity nonlinear mapping is converted into most efficient shift and additive operation of bottom hardware, and the method has the advantages that the complexity of the hardware is reduced, and the reliability of the hardware is improved. The area and the power consumption of the circuit are greatly reduced, and the feature extraction capability of the model is maintained.

The application discloses a traffic sign detection method based on an adaptive double-path local-global fusion network and belongs to the technical field of computer vision and intelligent traffic. Three innovative modules work together: the nonlinear adaptive feature extraction module adopts a wide-narrow parallel structure, combines an adaptive deformation activation function and a partial convolution mechanism, and enhances the capture ability of weak features; the dynamic channel calibration attention module realizes dynamic calibration of key channels through a local-global double-path interaction strategy and double-view attention fusion; and the local-global feature fusion module efficiently fuses multi-scale features based on multi-granularity parallel attention and reparameterization convolution technology. The application constructs an end-to-end detection network on the basis of a YOLO framework, significantly improves the precision and environmental adaptability of traffic sign detection, balances the calculation efficiency and resource consumption through light-weight design, and is suitable for high real-time requirement scenes such as automatic driving and intelligent traffic systems.

The application discloses a kind of dynamic channel-based numerical control system M code control method and related equipment, it is related to numerical control machine tool and industrial automation control technical field.Numerical control equipment first extracts M code set from current program segment, then according to preset channel distribution strategy, multiple M codes in M code set are distributed to different preset logical channels.M code is delivered according to channel distribution table, avoid the complex decoding logic problem caused by writing order uncertainty in traditional multiway control, PLC program only needs to process specific range of M code for fixed channel, reduce the complexity and memory occupation of ladder diagram program.Effectively solve the technical problems that the PLC program complexity is high in the prior art due to the coupling of M code channel allocation and writing order depth, while improving parallel processing efficiency, ensure the reliability of program execution, and then realize the scalability and maintainability of numerical control system by improving the parallel processing capacity of numerical control system.

Instead of having more lanes than the bus can support, the number of lanes can match the number the bus can support. In a system with six lanes and two ports, this means each port has two permanent lanes and two lanes shared with the other port. When changing the configuration from four lanes on the first port to four lanes on the second port, the shared lanes are put into a low-power state from the first port's perspective and into a fully operational state from the second port's. Thus, each port assumes there are four lanes available for it, resulting in a total of eight lanes for the device. In reality, however, there are only six lanes available for the device, thereby saving costs and meeting bandwidth and power limitations.

The present application belongs to the technical field of speech conversion, and in particular relates to a singing speech conversion method based on CBAM and dynamic convolution decomposition, which comprises a training stage and a conversion stage, and a model network comprises a generator, a discriminator and a style encoder. First, dynamic convolution decomposition is introduced in the generator, dynamic channel fusion is used to replace the dynamic attention of the channel group, the problem that dynamic convolution in the generator causes the number of convolution weights to increase by K times is solved, the difficulty of joint optimization is reduced, and the model requires fewer parameters without sacrificing accuracy, thereby improving the running performance of the algorithm. Further, CBAM attention modules are introduced in the encoding network and the decoding network of the generator, attention is applied to the channels and the space, the attention and capture of the detailed information in the spectrum are improved, and the quality of the converted singing speech is significantly improved.

This invention discloses a secure authentication method and system based on a general server interface, belonging to the field of interface security control technology. The method involves acquiring the original request message, extracting the interface identifier, timestamp, request parameters, and a first signature value; generating an interface context feature code based on the interface identifier and an interface mapping table, containing call hierarchy and permission domain information; and comparing this with a second signature value generated using a pre-shared key to determine message legitimacy. After confirming message legitimacy, a dynamic channel identifier bound to the interface context feature code is generated and written into the call context stack. The consistency between the dynamic channel identifier and the interface path vector is verified using a Merkle path binding verification algorithm, controlling the execution of the target business module. This invention achieves strong binding between interface message authentication and the server's internal call path, effectively preventing cross-module replay and path mismatch risks, and improving the security and reliability of general interfaces in complex operating environments.

The application relates to the technical field of communication signal processing, and provides a parallel receiving and processing device and method for multi-system signals based on dynamic configuration, wherein through the cooperative design of a data receiving module, a channel scheduling module, a tracking despreading module and a decoding module, a limited number of general signal processing channels are designed into a dynamic channel resource pool, dynamic allocation and recovery according to decoding results are supported, the decoupling of channels and signal systems is realized, and the resource utilization rate is greatly improved. A hardware scheduling module is realized in an FPGA, fast scheduling and parameter configuration are realized. The scheduling strategy is flexibly configured, the priority allocation of channel resources of key signals is guaranteed, each general channel is designed in a parameterized mode, different system signals can be seamlessly switched, FPGA logic does not need to be reconfigured, parallel tracking and despreading are realized, signal receiving and processing with multiple systems, multiple channels, high concurrency and low resource occupation can be realized in a single machine, and the system reliability is improved.

The application discloses a dynamic channel attention-based lightweight agricultural pest detection method and system, and particularly relates to the field of agricultural video target detection, and is used for solving the problem that channel weights are difficult to dynamically adjust with interframe changes in pest identification. The scale jump judgment of texture and contour features is used, continuous coefficients generated by a Gaussian process are taken as weight split gates, channel mapping and asymmetric transition are synchronously responded to interframe texture burst and form breaking, weight real-time rearrangement is realized, noise channels are rapidly inhibited through exponential contraction, high-confidence channels are stabilized through linear expansion, and it is ensured that the lightweight backbone maintains focusing on the smallest pests in power-limited occasions; the prior weight of a front frame is injected into subsequent mapping, the detection head always receives the latest effective features, and stable inference is maintained under light, shielding and parallax variation; meanwhile, delay caused by redundant splitting is avoided, deep convolution storage is saved, and original frame rate output is maintained.

The present application relates to the technical field of data fusion, in particular to a multi-source electrocardio signal correction method and system based on adaptive fusion, comprising the following steps: constructing a multi-channel input tensor, extracting local features by using a weight calculation network, adaptively weighting and fusing and reducing dimensions of the multi-channel signals, establishing a nonlinear mapping relationship by using a deep reconstruction network, reconstructing a standard waveform, and iteratively optimizing network parameters based on reconstruction error. In the present application, local neighborhood features of multi-channel signals are extracted by constructing a weight calculation network, a dynamic channel weight sequence reflecting real-time contribution of signal sources is constructed, amplitude intensity is adaptively adjusted according to signal quality, unstable channel noise interference is effectively suppressed and high-quality signal components are enhanced, a nonlinear feature transformation of the fused sequence is performed by using a deep reconstruction network, an accurate mapping from non-standard input to standard lead waveform is established, and weight distribution and signal reconstruction parameter optimization are realized in combination with an error back propagation mechanism.

The invention discloses a multi-element echelon soft and hard combined unmanned aerial vehicle countering system and method, and relates to the technical field of unmanned aerial vehicle defense. The method comprises the following steps: identifying a behavior intention and an anti-interference level of an unmanned aerial vehicle through information fusion of a multi-source sensor; according to an identification result, generating a plurality of candidate countering strategy chains containing different soft and hard means combinations and time sequences in parallel; executing a first step means of the selected chain, and synchronously calculating an actual interference efficiency index; comparing the actual efficiency with an expected target to generate an efficiency deviation signal, and dynamically deciding and adjusting subsequent parameters or switching a strategy chain through a finite-state machine based on the signal; through a dynamic channel guiding mechanism, the unmanned aerial vehicle is restrained in a dynamically generated expected interception channel by a soft interference means, and hard interception is triggered according to a predicted trajectory in the channel. According to the method, the problems of rigid response mode and insufficient means collaboration in the prior art are solved, and integrated countering from sensing, decision making to self-adaptive processing is realized.

This invention relates to a multi-channel abnormal flow detection method for power systems based on an adversarial autoencoder. By employing protocol-application dual-dimensional fine-grained dynamic channel partitioning, three-stage progressive collaborative training within dedicated channels, and a memory-enhanced reconstruction mechanism with Top-k sparse constraints, it effectively addresses core shortcomings in existing technologies, such as feature interference caused by coarse-grained channel partitioning, model instability due to missing three-stage training, and abnormal smooth reconstruction caused by over-generalization of ordinary autoencoders. The solution achieves accurate modeling under business semantic isolation, explicit anchoring of normal patterns, and high-sensitivity amplification of subtle deviations. While maintaining an extremely low false alarm rate, it significantly improves the detection capability for high-risk, low-frequency disguised anomalies. Furthermore, through an online channel adaptive evolution mechanism, it endows the system with the ability to autonomously adapt to dynamic changes in power grid business over the long term, meeting the stringent requirements of power systems for high reliability and real-time performance.

The invention discloses an improved U-Net network model and an application method thereof in physical field prediction, on the basis of a standard encoder-decoder architecture, a strong attention mechanism is fused, and the method comprises the following steps: applying a static space attention module which is based on priori knowledge and has non-training updated parameters to a preset key area at a network input end, focusing on the specific region with a forced model; and a dynamic channel attention module which can automatically learn feature channel importance and has parameters updated along with training is integrated in a network bottleneck layer. According to the method, engineering priori knowledge and data-driven feature learning are combined, so that the prediction precision of the model on complex physical fields such as blade top temperature distribution under the small sample condition is remarkably improved, and particularly, the prediction accuracy on a high-temperature region is enhanced.

The invention discloses a flying probe parallel test method and system based on dynamic channel allocation. The flying probe parallel test method based on dynamic channel allocation comprises the following steps: analyzing a test program to dynamically group a plurality of test tasks into at least one parallel test task group; controlling a plurality of flying probes corresponding to the task group to move at the same time and contact a test point; synchronously switching a plurality of test instrument channels to the plurality of probes while the probes are in place; sending a synchronous trigger signal to the test instrument so as to execute measurement at the same time; according to the method, the measurement data is recovered in parallel and the plurality of probes are controlled to be lifted at the same time, and the traditional test mode is upgraded from N-time serial to N / M-time parallel, so that the total moving times of the probes and the frequent switching operation between instruments are greatly reduced in the test process, and the overall test efficiency is improved by several times.

This invention provides a general EEG signal representation learning system based on a dynamic multi-scale mechanism, comprising a data preprocessing and enhancement module, a multi-scale block embedding module, and an encoder incorporating a dynamic decomposition attention mechanism, connected sequentially. Training is performed using a hybrid generative-discriminative self-supervised pre-training framework. The data preprocessing and enhancement module segments and standardizes the input continuous EEG signal, and applies a probability enhancement strategy to generate two enhanced views of the same signal. The multi-scale block embedding module processes the input signal using at least two sets of parallel convolutional kernels of different scales to generate an initial latent representation. The encoder incorporating the dynamic decomposition attention mechanism sequentially performs temporal causal modeling and dynamic channel grouping spatial attention modeling on the initial latent representation. This technical solution is adaptable to different acquisition devices, captures multi-scale time-frequency features, and possesses excellent linear separability.

The invention discloses a self-supervised training generation method suitable for a multivariable time series prediction pre-training model, and the method comprises the steps: screening a pre-training data set based on morphological similarity; the method comprises the following steps: comparing a representative time sequence of a data set to be screened with a time sequence of a target power source load (wind power, photovoltaic and load) in periodicity, non-negative fluctuation, stability and other dimensions, and selecting a data set with similar features with the time sequence of the target source load as a pre-training data set; then, a dynamic channel mask rate mechanism is introduced in a multivariable self-supervised training stage, only part of channels are randomly selected to participate in loss calculation in the initial stage of training, the proportion of the participating channels is gradually increased according to preset scheduling along with the iteration process, and finally transition to full-channel joint prediction is conducted. Compared with the prior art, the method can improve the matching degree of the pre-training data and the target task, improves the convergence stability and prediction precision of the multivariate pre-training model, and reduces the development and application cost of the model.

The application discloses a few-channel SSVEP dynamic channel weight fusion adaptive thought typing system and belongs to the technical field of brain-computer interfaces. The system comprises a stimulation display unit, an electroencephalogram signal acquisition unit, a channel configuration adaptation unit, a channel contribution double-layer evaluation unit, a multi-algorithm historical adaptive fusion decoding unit and a knowledge distillation and rapid adaptation unit. The channel configuration adaptation unit supports plug-and-play dynamic reconstruction under the condition of arbitrary change of 6-10 channels; the channel contribution double-layer evaluation unit fuses physical signal quality and algorithm contribution, dynamically weights and eliminates invalid channels; the multi-algorithm historical adaptive fusion decoding unit dynamically integrates CCA, FBCCA and TRCA algorithms based on the sliding window historical accuracy rate; and the knowledge distillation and rapid adaptation unit provides a cross-channel pre-training model and 3-5 minute minimal fine-tuning. The application realizes the cross-configuration technical goal of few-channel SSVEP thought typing.

Rather than having more lanes than the bus can support, the number of lanes can match the number of lanes the bus can support. For a six lane, two port system that means each port will have two permanent lanes and two lanes that are shared with the other port. When changing configurations from four lanes on the first port to four lanes on the second port, the shared lanes are placed in low power stage from the perspective of the first port and moved into full operational stage for the second port. Thus, each port believes there are four lanes for the port for a total of eight lanes for the device. However, in reality there are only six total lanes for the device, thus saving costs and matching bandwidth and power limitations of the device.

The invention provides a fault diagnosis method and system for a suspension system of a high-speed maglev train, and belongs to the technical field of fault diagnosis based on a neural network model. The to-be-processed data are original CAN bus data and equipment communication messages of a suspension system collected by a real-time monitoring network covering suspension units of the whole train when the high-speed maglev train runs; and processing the acquired to-be-processed data by using a pre-trained fault diagnosis model to obtain a fault diagnosis result of the suspension system of the high-speed maglev train. According to the method, through cooperation of CNN feature enhancement, block decision, reversible normalization and dynamic channel attention, three-dimensional balance of calculation efficiency, diagnosis precision and industrial suitability is realized; the defects of low long sequence modeling efficiency and high rule dependency degree are overcome; the calculation cost and the feature distortion risk are reduced, and dependence on manual frequency domain features is not needed.

A system and method for fixed size texture compression utilizing dynamic alpha channel compression is presented. The method includes determining a first endpoint of a pixel block including four pixels, each represented by a plurality of channels; determining a second endpoint of the pixel block; encoding the first endpoint and the second endpoint using a total of 31 bits; encoding a first quantization level using a second plurality of bits; encoding a second quantization level using a third plurality of bits; encoding an alpha map of the four pixels using a fourth plurality of bits; encoding a location of the first endpoint and a location of the second endpoint using a fifth plurality of bits; and storing an addressing indicator bit based on a distance between a value of the first endpoint and a value of the second endpoint.

The application discloses a double-flow fault diagnosis method based on state space modeling and dynamic edge context perception, which constructs a time domain state flow and a time-frequency edge flow, and the specific implementation process is as follows: the time domain state flow is modeled in a Transform framework to form a MambaT encoder for efficiently capturing long and short term dependencies with linear complexity and accurately representing time domain sensitive features; the time-frequency edge flow transforms vibration data into a two-dimensional time-frequency image through wavelet transform, designs a differentiable edge sensitive mask to adaptively separate impact features and background noise of the time-frequency image, simultaneously designs a boundary content perception adaptive padding to maintain the topological integrity of the image boundary, and cooperatively constitutes a dynamic edge context perception convolution to effectively extract time-frequency edge features; a channel enhancement and channel gating module is designed to establish a dynamic channel interaction gating mechanism, realize time domain-time frequency domain double-flow cascaded representation, and significantly improve fault discrimination ability. Experiments prove that the method has higher fault diagnosis performance.

The embodiment of the invention provides a video noise reduction method and device, a medium and equipment, and the method comprises the steps: determining the inter-frame motion intensity of an original video, constructing a key frame group of the original video, and constructing the key frame group of the original video according to the inter-frame motion intensity. According to the key frame group, obtaining dynamic channel characteristics for motion noise reduction through frequency domain conversion and channel attention weighting; static global features of space details in the original frame are determined and reserved; extracting semantic features of the original frame; and according to the semantic feature of each original frame, the static global feature of each original frame and the dynamic channel feature, obtaining a noise-reduced video through feature fusion. According to the method, through inter-frame motion intensity, key frame group construction, dynamic and static dual-feature collaborative noise reduction and semantic feature fusion, the original video after noise reduction has no motion blur and ensures space details.

The application discloses a shared variable dependency chain break detection method and device, relates to the technical field of multiprocessor programming, and comprises the following steps: coupling two detection channels of a static reverse assembly channel and a dynamic PMU channel, wherein the static reverse assembly channel utilizes the principle that the relevant reverse assembly code will change when the shared variable dependency chain breaks, the dynamic PMU channel utilizes the principle that the number of relevant PMU events in the program running process will change when the shared variable dependency chain breaks, a shared variable dependency chain break detection architecture is constructed, and the technical problems that the performance overhead of a dynamic analysis method is large, and the automation degree of a static analysis method is limited in the related art are solved. In addition, the coverage scene of a formal verification method is limited, and it is difficult to reproduce the complex competition conditions of a production environment, technical effects that the accuracy and efficiency of detection are effectively improved, the dynamic detection overhead is reduced, and the competition conditions in a real production environment can be covered are achieved.