1007 results about "Time windows" patented technology

The invention relates to the technical field of cooperative control, in particular to a multi-machine cooperative control method and system for a bridge cable hoisting device, and the method comprises the following steps: collecting tension change characteristics through a distributed sensor array, extracting indexes through a sliding window algorithm to generate a response time window, and generating a cooperative time window through time sequence overlapping and speed trend adjustment. Calculating a compensation value by combining inertial parameters and acceleration to generate a trigger window, detecting tension and displacement difference, inputting the tension and displacement difference into a dynamic threshold function for screening and fusion, outputting a synchronous trigger condition, and generating a synchronous control instruction by encoding a device number and a tension change rate after timestamp alignment. According to the method, multi-node tension synchronous acquisition and normalization processing, dynamic response generation through a sliding window, boundary matching action triggering adjustment through an overlapping algorithm, response lag correction through inertia compensation, tension and displacement difference real-time detection, dynamic threshold screening and triggering condition fusion and timestamp alignment packaging instruction consistency are carried out; and the cooperation precision and the system robustness are improved.

The invention provides a supercomputing resource intelligent allocation method and system based on AI load monitoring, and the method comprises the steps: firstly obtaining a real-time load data set of a plurality of computing nodes in a supercomputing system, including a resource occupancy rate index and a task queue state parameter, then carrying out the load feature extraction processing of the real-time load data set, generating resource occupancy fluctuation characteristics and task queue evolution characteristics, performing resource demand analysis on the load characteristic set by using a pre-constructed resource demand prediction model, predicting resource demands of each computing node in a subsequent time window, determining resource allocation priorities and migration strategy parameters based on prediction results, and performing resource allocation according to the resource allocation priorities and the migration strategy parameters. The resource dynamic allocation instruction is generated and sent to the target computing node, the resource reallocation operation is triggered, intelligent and efficient allocation of super computing system resources is achieved, the resource utilization rate and the task execution efficiency are improved, and the overall energy efficiency ratio and stability of the system are optimized.

A kernel monitor can be used to mitigate ransomware activity of a host system. In some aspects, a computing system can use the kernel monitor to monitor a set of system calls generated by the host system within a time window to perform a functionality. The kernel monitor can include a respective kernel program monitoring each system call in the set of system calls. The set of system calls can be filtered by the kernel monitor to identify a subset of system calls associated with encrypting a filesystem of the host system. The computing system can determine that the subset of system calls is indicative of ransomware activity associated with the host system based on the subset of system calls exceeding a predefined threshold. Subsequently, the computing system can perform a mitigation operation to mitigate the ransomware activity.

The invention relates to the technical field of edge computing, in particular to a safety production monitoring method based on edge collaboration, which comprises the following steps: collecting multi-node clock pulses, calculating adjacent time differences to form a pulse difference parameter set, carrying out statistical distribution, setting a dynamic tolerance boundary, and constructing an effective deviation parameter set; the method comprises the following steps of: generating a weighted smooth deviation result by combining clock stability and link delay weighted normalization, sorting monitoring data according to a timestamp to identify duplication abrupt change exception, constructing a cross-node exception time sequence by associating nodes and time information, and judging a safety state transition result of equipment. A time difference parameter is constructed through multi-node reference pulse arrival time, a dynamic deviation constraint is formed by combining distribution characteristics, clock stability and link measurement weighted smoothing are introduced, cross-node time reference consistency is kept, operation data unified time sequence rearrangement is carried out, a sudden change event reliably reappears in a time window, and an abnormal identifier is associated with a node identity. And forming a cross-node event evolution link.

The invention relates to the technical field of blockchain network security monitoring, and particularly discloses a blockchain-based network security monitoring method and system, the system collects transaction data, block data and intelligent contract execution results from a plurality of blockchain nodes, the analysis technology calculates the abnormal degree of each time window, the change rate of data inconsistency is quantified, and the data security monitoring accuracy is improved. If a relatively high data inconsistent change rate is detected, further comparing consensus results of the nodes, calculating consensus verification differences of the nodes, identifying the nodes deviating from normal consensus behaviors, constructing a machine learning model of a support vector machine, evaluating the safety states of the nodes, and according to a result output by the model, determining the inconsistent change rate of the nodes. The safety states of the nodes are divided into normal, slight abnormality and serious abnormality, for the nodes marked as slight abnormality, the system analyzes the abnormality trend and deviation of the nodes through a time sequence, automatically marks high-priority concerned nodes and generates a review task list to remind an operator to intervene.

The invention discloses a computing power distribution method based on a dynamic window and multi-version correction, and relates to the technical field of intelligent resource scheduling, the method comprises the following steps: constructing a scheduling state set by monitoring a to-be-scheduled task set and a platform resource state of a cloud platform resource scheduling system in real time; based on time window dynamic judgment, extracting a stable and effective task set; calculating a scheduling priority coefficient in combination with task resource intensity, urgency and historical completion performance, determining a priority scheduling set and executing a first version scheduling scheme; the difference between the current scheduling version and the historical optimal scheme is evaluated through the residual vector and the offset coefficient, and whether version correction needs to be carried out or not is judged; and based on resource use feedback in the scheduling execution process, calculating a feedback change rate and judging whether a scheduling execution state is stable or not, and if the scheduling execution state is unstable, dynamically adjusting scheduling parameters. According to the method, accurate allocation of computing power resources, adaptive scheduling optimization and abnormal risk control are realized.

The invention discloses a method for processing an uncorrectable error of bus equipment and a server, relates to the technical field of servers, and aims to reduce the reporting frequency of the uncorrectable error by setting a suppression identifier of the uncorrectable error of the bus equipment and a time window of uncorrectable error statistics, and improve the reporting efficiency of the uncorrectable error when the uncorrectable error is triggered for the first time. According to the method, uncorrectable errors are reported, the uncorrectable errors are captured, it is ensured that initial errors are not missed, and when the error count of a time window is larger than a reporting threshold value, reporting is carried out again, error missing processing of error aggravation is avoided, redundant error reporting is reduced, an error reporting storm is inhibited through an inhibiting identifier, and the error reporting efficiency is improved. Therefore, the influence of frequent error triggering on the availability of a server system and normal operation of other services is reduced, the reliability of the server is improved, and the technical problems that frequent error triggering cannot be corrected, other service requirements are covered and the availability of the server system is influenced in the related technology are solved.

The invention discloses a heterogeneous computing power resource dynamic cooperative scheduling method and system and a computer device. The method comprises the steps of obtaining multi-dimensional feature information of a to-be-scheduled task, current state information of each node in a heterogeneous computing power resource pool and scheduling environment state information; training a time sequence model by adopting historical associated data, inputting the multi-dimensional feature information, the current state information and the scheduling environment state information into the trained model to obtain associated trend information in a future preset time window, and then combining the multi-dimensional feature information and the current state information of all the nodes to obtain the scheduling environment state information. A multi-objective optimization function fusing task delay, cost consumption and energy consumption is constructed, the function is optimized based on a preset strategy, a target decision strategy is obtained, a scheduling instruction set is generated after analysis, allocation information and target nodes are determined according to the scheduling instruction set, and tasks are issued. According to the method, dynamic collaborative scheduling of heterogeneous computing power resources can be realized, and the overall utilization efficiency of the resources is accurately, efficiently and effectively improved.

The invention discloses an intelligent substation secondary equipment maintenance control method and system, and relates to the technical field of secondary equipment maintenance control, and the method comprises the following steps: obtaining first data of a substation to construct a static matrix, and generating a dynamic topological graph; determining the security level of the controlled equipment according to the dynamic topological graph, and establishing a priority table of mapping of the equipment type and the security level; detecting concurrent instruction conflicts through a priority table, identifying an N-order virtual loop based on a dynamic topological graph, generating a minimum isolation set, and outputting a virtual terminal isolation instruction sequence with timing constraint according to a signal level; and when the state of the maintenance pressing plate or the GOOSE link is changed, the affected equipment is positioned based on the static matrix, and incremental updating of the dynamic topological graph is controlled according to the instruction sequence. According to the method, the path optimization strategy based on the cross-interval coefficient is matched with the time window constraint, so that the maintenance efficiency is guaranteed, potential safety hazards caused by disordered operation time sequences are avoided, and the maintenance process is standardized and intelligent.

The invention provides a stream computing resource scheduling method and system based on a dynamic time window, and relates to the field of stream computing resource scheduling, and the method comprises the following steps: constructing a master-slave event time monitoring network to obtain a task feature vector, and establishing an adaptive time window scheduling mechanism to dynamically adjust the window size; and constructing a multi-level task priority queue, carrying out state transition by adopting a remote direct memory access mechanism of incremental transmission, and finally coordinating computing nodes through a distributed task arrangement protocol to complete resource switching. According to the method, computing resource competition can be effectively reduced, the flow computing task processing efficiency is improved, dynamic optimal configuration of resources is realized, and the system throughput is enhanced.

The invention provides a high-speed rail tunnel safety monitoring method and system. The method comprises the following steps: carrying out normalization processing on original time series data to construct a multi-dimensional time series data set; the multi-dimensional time sequence data set is input into a pre-trained LSTM model, the structural state of the tunnel key part in a future time window is predicted, and the structural state comprises the surrounding rock deformation trend, the lining stress peak value, the water seepage risk grade and the crack severity grade; and according to the prediction result of the structure state and a preset mapping relationship between the structure state and the early warning level, triggering hierarchical early warning, and generating a corresponding tunnel maintenance strategy instruction. According to the technical scheme provided by the invention, intelligent sensing, accurate prediction and active maintenance decision making of the tunnel structure state can be realized, and the timeliness and preventability of high-speed rail tunnel safety management are improved.

The invention discloses a process monitoring method, system and device for a baseboard management controller and a medium, and relates to the technical field of server management, and the method comprises the steps: continuously collecting CPU occupation information of a target process from an information collection file through a kernel interface based on a preset sampling time interval, and calculating the CPU utilization rate of the target process, and calculating a target parameter based on the CPU utilization rate in the preset sliding time window, analyzing the target parameter and the CPU utilization rate based on a preset threshold value, and determining the CPU state of the target process. According to the method, the CPU usage rate of each process can be continuously calculated by periodically sampling the CPU occupation information and the system CPU time of the target process, the CPU usage condition of the target process is analyzed based on the sliding time window, sampling and analysis can be automatically carried out, whether abnormity exists or not is monitored in real time, and the monitoring efficiency of the target process is improved.

The invention relates to a time series data acquisition method and device based on a dynamic time window and fingerprint deduplication. The method comprises the following steps: establishing a sliding time window with a variable length by taking the current system time as a reference, and only querying incremental time sequence data in the window; adaptively zooming the window by calculating the ratio of the collected data volume to the expected volume in real time; setting the trigger interval as half of the length of the sliding time window to form a time overlap, thereby capturing out-of-order data; key features of each piece of time series data are extracted, and data fingerprints are generated; a bloom filter is used for primary screening, and accurate duplicate removal is carried out in a key-value storage system; compared with an existing fixed polling scheme, the method has the advantages that resources can be dynamically adjusted along with data flow rate, system load is reduced, real-time performance is improved, data integrity is guaranteed by overlapped windows, zero repeated acquisition is guaranteed by fingerprint two-stage duplicate removal, and the method is applicable to efficient incremental synchronization scenes of transactional log-free time sequence databases such as InfluxDB and the like.

The invention discloses a network equipment fault analysis method and device based on multi-dimensional characteristics, and relates to the technical field of network equipment fault analysis, and the method comprises the steps: obtaining the time sequence characteristic data of each network equipment in an Internet of Things cluster during the operation of a current time period, and forming a multi-dimensional network fault identification data set according to the time window dimension, the network equipment dimension and the feature dimension, carrying out encoding processing on the network fault identification data set by adopting a pre-trained encoder to obtain an encoding vector, inputting the encoding vector into a pre-constructed fault prediction decoder, and carrying out fault prediction on the network fault identification data set according to the pre-constructed fault prediction decoder. And outputting the fault probability of each network device in the preset time period. The network equipment fault diagnosis method and device solve the technical problems that when network equipment fault diagnosis is carried out in the related technology, due to the fact that network faults have space-time variability and diversity, single-dimension analysis cannot accurately capture the full view of the faults, and fault diagnosis is delayed and misdiagnosed.

The invention discloses a computing power server load prediction method and system, and belongs to the field of data processing, the computing power server load prediction method comprises the following steps: collecting historical load data of a computing power server, and preprocessing the historical load data, the preprocessing comprising data cleaning, time window construction, and data set division; constructing an LSTM (Long Short Term Memory) model, and training the LSTM model; predicting a future computing power server load through an LSTM model; compared with the prior art, the method has the beneficial effects that the overall computing resource utilization rate is effectively improved by collecting the historical load data of the computing power server, training the LSTM model, finally predicting the future computing power server load through the LSTM model and allocating the computing resources according to the predicted data; meanwhile, abnormal tasks are recognized, and the abnormal tasks are processed in time when appearing, so that other tasks are prevented from being affected.

The invention discloses an operation data processing method and system based on a compliance verification library and a risk control plan, and belongs to the technical field of data compliance and risk control, and the method comprises the steps: obtaining an execution plan of a target operation task and risk control strategy information of the execution plan, analyzing operation data items, distributing corresponding compliance verification time windows and levels, and forming an operation data verification scheduling table; according to the verification level and the business domain, a rule set is extracted from a compliance verification library, and rules comprise predefined rules, model output rules and historical violation rules; field-level matching, logic consistency verification and cross-source linkage verification are executed in the verification time window; when the verification result meets the triggering condition, generating an abnormal event, and executing response operations such as delay, recheck or interception; and finally, writing the execution state and the verification result into an abnormal log queue for risk control strategy parameter updating or re-evaluation. According to the method, the dynamic compliance verification and strategy closed-loop optimization of the operation data are realized, and the intelligent compliance control capability of the system is improved.

The invention discloses a computing task scheduling method and system based on time sequence diagram network resource state prediction, and relates to the technical field of computing resource allocation, and the method comprises the steps: collecting computing power resource state data based on computing power nodes, and modeling the computing power resource state data into time sequence heterogeneous diagram data; constructing a time sequence heterogeneous graph attention network THGAT model, and performing offline training on the THGAT model by taking node feature and edge feature prediction loss as a loss function and taking minimization of a prediction error as a target; inputting the time sequence heterogeneous graph data into a THGAT model, and predicting the resource state of a future time window; the calculation task is converted into a calculation demand vector, and a multi-objective optimization resource allocation THGATRM algorithm is adopted to perform calculation power resource allocation on the calculation task according to the resource state of the corresponding time window; and executing the computing task according to the computing power resource allocation scheme. Through the technical scheme of the invention, the accuracy of computing power resource prediction is improved, and efficient allocation and optimization of resources are realized.

The invention relates to a server-side satellite precision product anomaly detection method and system and a terminal, specifically, a previous time window of a current epoch is obtained, the time window comprises a plurality of epochs, the residual mean value and the root-mean-square of each satellite in each epoch in the time window are calculated, and the residual mean value and the root-mean-square of each satellite in each epoch in the time window are calculated; normalizing the residual mean value and the root-mean-square to obtain the characteristics of the satellite in epochs; acquiring features of all the satellites in all epochs in the previous time window to form a feature set, performing outlier detection on the feature set by adopting an LOF method, and determining the LOF score of the satellite at the current epoch moment by utilizing the features of the current epoch of the satellite and the feature set after removing the satellite of which the LOF score is greater than a preset value from the feature set; the LOF scores of the satellites are sent to the PPP user side, when the PPP user side carries out ambiguity fixing, all the satellites are tried to be fixed firstly, if fixing fails, the satellite with the maximum LOF value is removed from the ambiguity set, fixing is tried again, and continuous circulation is carried out till the ambiguity is fixed successfully.

The invention discloses a VAGCN-based complex industrial process operation state evaluation method. The method comprises the following steps: collecting process data and carrying out standardization processing; performing data division on the data by using a sliding window to obtain a time sequence; the method comprises the following steps: converting time sequence data into cross-time-space variable interaction graph structure data through a variable interaction graph structure, and then capturing variable interaction characteristics and local space-time dependency in each time window by using a moving window; time pooling is used, advanced features of variable interaction are extracted and then input into the operation state evaluation model, posterior probabilities of the online data belonging to different operation state evaluation grades are obtained, and a final evaluation result is a state grade corresponding to the maximum posterior probability at the current moment; and performing similarity analysis on the variable interaction graph structure in the non-optimal state and the optimality graph structure in the optimal state so as to evaluate the variable dynamic offset degree in the non-optimal state. According to the method, the interaction among the variables at different time points can be captured, and more accurate process operation state evaluation can be realized.

The invention discloses a micro burst traffic detection method, network element equipment, a computer readable storage medium and a computer program product, and relates to the technical field of communication, the method is applied to the network element equipment, the network element equipment is provided with a microsecond-level timer, and the method comprises the following steps: counting the number of forwarding message bytes of each sub-time window in a sliding time window; performing mean value calculation on the forwarding message byte numbers of all the sub-time windows in the sliding time window to obtain a sub-window byte number mean value of the sliding time window; determining a sub-window byte number threshold value of the sliding time window based on the sub-window byte number mean value; and determining the state of the sub-time window of which the forwarding message byte number is greater than the byte number threshold value of the sub-window as a flow micro-burst state. According to the invention, the size of the micro-burst flow and the corresponding time delay can be accurately detected in a fine-grained manner, and the detection precision of the micro-burst flow is effectively improved.

Techniques are described for deploying software applications associated with document management. The techniques may include a system comprising processing circuitry implemented by one or more processors, the processing circuitry having access to a memory. The processing circuitry may be configured to determine, for a time window, a current version of a widget and a next version of the widget to implement a software application of the one or more software applications. The processing circuitry may further be configured to output instructions to execute the current version of the widget in response to receiving a first request for the software application. The processing circuitry may further be configured to output instructions to execute the next version of the widget based on a number of responses to previous requests that select the current version of the widget.

The invention relates to the technical field of graph number synchronization, and discloses a flight equipment graph number synchronization method and device under a time window verification mechanism, and the method comprises the steps: carrying out the demultiplexing operation of the video stream data, obtained in real time, of flight equipment, and obtaining a plurality of video frames corresponding to the video stream data and a frame timestamp corresponding to each video frame; performing target processing operation on all the video frames to obtain a metadata set corresponding to all the video frames, and determining a timestamp corresponding to each piece of metadata in the metadata set; and according to the frame timestamps corresponding to all the video frames and the timestamps corresponding to all the metadata, performing time window verification operation on all the video frames and all the metadata to obtain synchronous metadata corresponding to all the video frames. Therefore, by implementing the method and the device, time window verification can be performed on the timestamps between the video frames and the metadata, so that the synchronous metadata can be matched for the video frames, and improvement of image and data synchronization precision of flight equipment and reduction of image and data transmission delay are facilitated.

The invention discloses a multi-layer heterogeneous sequential network feature alignment and clustering method and device based on tensor self-representation. The method comprises the steps that sequential feature matrix sequences {},..., {} of data views in a multi-view heterogeneous network in time windows are obtained; based on the matrix sequence of the first time window, constructing a Laplacian regular term by introducing a self-representation learning mechanism, modeling multiple views by adopting a tensor structure and introducing a Schatten p-norm regularization mode to construct an optimization objective function for optimization, and representing shared feature representation of each data view in the first time window; and solving the function and carrying out clustering processing on the obtained function, wherein the clustering result of each time window is used for carrying out characteristic analysis on nodes in the network. According to the method, on the premise that the original structure and semantics of the network are guaranteed, unified modeling can be carried out on the incomplete multi-view heterogeneous network, and feature expression is aligned.

The invention relates to an access control method and system for an industrial internet server. The method comprises the following steps: analyzing a plurality of round-trip delays and a plurality of response delays to obtain a failure coefficient of a target equipment execution instruction; determining an execution delay index according to the deviation and the failure coefficient between the response duration of each process variable of the target equipment for the plurality of instructions in the same time window and the corresponding preset duration; according to the relevancy between the execution delay indexes of the time windows and the change characteristic values of the process variables, the influence index of instruction delay execution on the process variables is determined; calculating a risk coupling coefficient corresponding to the instruction sequence according to the delay difference of each adjacent instruction in the instruction sequence; and adjusting the number of access instructions of the server according to the influence index and the risk coupling coefficient. According to the method, the interlocking amplification risk of instruction delay and process variable fluctuation can be effectively inhibited, and the stability and reliability of an industrial internet access control system are guaranteed.

The invention discloses an asynchronous hierarchical federated learning method, system, device and medium, and relates to the technical field of federated learning, and the method comprises the steps: clustering devices with similar training time in the same cluster at a client-edge end by using a mode based on a dynamic time window, and carrying out the clustering of the devices in the same cluster; dynamically grouping the equipment and asynchronously receiving local model update uploaded by the client; and at the edge end-central server, the performance evaluation and the historical participation degree of the edge server are dynamically scored, and the weight of the edge server participating in the global model aggregation is dynamically adjusted according to the score, so that a plurality of edge aggregation models are subjected to new global aggregation to obtain the global model; according to the method, the equipment is dynamically grouped, the local model is updated, the problem of equipment heterogeneity in the equipment is solved, the weight of the edge server participating in global model aggregation is dynamically adjusted according to the score, the heterogeneity condition existing in data is counteracted, and the federal learning aggregation efficiency is greatly improved.

A method performed by a wireless transmit / receive unit (WTRU) may comprise: receiving, from a base station, configuration information; receiving, from an application layer, one or more uplink protocol data units (PDUs); determining a buffering delay for transmitting the one or more uplink PDUs in resources associated with configured grant (CG) physical uplink shared channel (PUSCH) occasions; and on a condition that the buffering delay is greater than a delay threshold and that there are no available resources associated with semi-persistent scheduling (SPS) physical downlink shared channel (PDSCH)occasions within a time window, transmitting an indication of a time offset for the time window. The method may further comprise transmitting, to the base station, in a CG PUSCH occasion, the one or more uplink PDUs.

The invention discloses a data processing method, electronic equipment and a storage medium, and the method comprises the steps: setting a duration adjustment parameter according to the configuration information of a target sensor; when the prediction data monitored by the target sensor in the current time window is determined by using the historical data collected in the historical time window, obtaining an error value between the prediction data and the actual data collected in the current time window, and comparing the error value with the duration adjustment parameter; adjusting the first duration of the current time window to the second duration of the next time window according to the comparison result; the method comprises the following steps: screening first data sets collected in a historical time window, a current time window and a next time window by using a group of network models to obtain a second data set; through the method, the technical problem that the utilization rate of the storage space is low in the processing process of the time sequence data in the related technology is solved, and the technical effect of saving the storage space in the server is achieved.

The invention discloses an early warning method and device for multi-device time sequence data and electronic equipment. The method comprises the steps of determining a time sequence data stream; dividing the time sequence data stream according to the model identifier to obtain a plurality of sub-time sequence data streams, sliding on the sub-time sequence data streams by using the time windows according to a preset step length from the starting timestamps of the sub-time sequence data streams, and determining the time sequence data under the time window which slides each time as a target time sequence data stream; performing timestamp alignment and data filling on time sequence data in the target time sequence data stream to obtain an aligned target time sequence data stream, and predicting a time sequence data predicted value corresponding to the aligned target time sequence data stream in a future time period through a prediction model corresponding to the aligned target time sequence data stream; and determining early warning information corresponding to the target time sequence data stream according to the time sequence data predicted value. The technical problem that the accuracy of an early warning result is affected by data time sequence dislocation caused by inconsistent acquisition time of time sequence data between devices is solved.

A method, system and electronic device for regulating the top cover pressure of a vertical hydro-generator set, and a storage medium. The method comprises: setting a monitoring time window, and collecting thrust bearing pad temperatures at different time points within the monitoring time window; acquiring a top cover pressure influence factor set at each time point; inputting the top cover pressure influence factor set corresponding to each time point into a pre-built top cover pressure influence correction database for optimal matching to obtain a bearing pad temperature correction factor corresponding to each time point; using the bearing pad temperature correction factor corresponding to the same time point to perform temperature influence correction on the corresponding thrust bearing pad temperature so as to obtain a corrected thrust bearing pad temperature; and on the basis of a preset pad temperature threshold, performing over-temperature calculation on the corrected thrust bearing pad temperature at each time point.

The invention provides an industrial single-chip microcomputer automatic control method based on time sequence event triggering, and the method comprises the steps: carrying out the multi-period sampling and time window division of an original signal inputted by an industrial field sensor or a switching value, and extracting the time distribution characteristics of the signal; performing signal stability judgment and pseudo edge elimination based on the signal time distribution characteristics, and outputting a stable edge event set; performing event confirmation delay and multi-level logic judgment on the stable edge event set to generate a preliminary time sequence trigger signal; environment characteristic data are collected, real-time feedback of an actuator is obtained, and system judgment parameters are dynamically corrected; and generating a control command and verifying an execution result based on the preliminary time sequence trigger signal and actuator feedback, the method can dynamically distinguish real signals and noise under vibration interference, simultaneously ensures the real-time performance and trigger reliability of the system, and solves the problems of insufficient edge detection stability and false trigger phenomenon caused by mechanical vibration.