23 results about "Trend detection" patented technology

The invention relates to the technical field of numerical control system fault pre-diagnosis, and particularly discloses a fault monitoring method and system for a numerical control machine tool. The method comprises the steps that a spindle vibration signal, a cutting force signal and a spindle current signal are collected; outputting an alignment signal set; respectively executing adaptive filtering processing according to the alignment signal set; constructing a multi-dimensional feature vector; constructing a health index under the processing behavior period; then accumulated trend deviation analysis is executed through a CUSUM algorithm; and judging whether the tool wear trend is abnormal. The technical problem that in the prior art, static threshold judgment is carried out based on a single signal source, and especially in a numerical control machining scene with signal noise interference, accurate early warning of the early wear trend of a tool cannot be achieved is solved. Due to the fact that a period calibration mechanism fusing spindle vibration, cutting force and current signals, feature vector entropy weight optimization and a CUSUM trend detection mechanism are adopted, the pre-diagnosis level of the numerical control machine tool on cutter state changes is improved.

The invention discloses a battery SOH dynamic prediction method based on federated learning and working condition adaptive fusion. The method comprises the steps that an end-side heterogeneous multi-modal feature extraction mechanism is adopted to extract physical features and working condition features of each battery from a battery changing cabinet end and a vehicle-mounted end; a double-branch fusion prediction network is constructed, and the double-branch fusion prediction network is trained through cooperation of a battery changing cabinet end global aggregation mechanism and a vehicle-mounted end self-adaptive mechanism; processing the extracted features by adopting two branch networks in the trained double-branch fusion prediction network and a gating fusion mechanism to obtain a predicted state of health (SOH) of each battery; the method comprises the following steps: constructing a predicted SOH sequence of each battery, constructing a multi-time-scale early warning module through a short-term mutation detection method and a long-term trend detection method, carrying out judgment operation on the predicted SOH sequence of each battery, judging whether each battery is in a risk state or not, and outputting an alarm when the battery is in the risk state; and the SOH prediction precision and the early warning capability of attenuation abnormity are improved.

The invention provides an event detection method and system for a mechanical overspeed test of a steam turbine generator unit, relates to the technical field of state monitoring and fault diagnosis of electric power equipment, and aims to solve the problems that existing mechanical overspeed test recognition is prone to false alarm and missing alarm, and positioning at a critical moment is not accurate. The method comprises the following steps: acquiring the active power of a generator and the rotating speed of a steam turbine, carrying out preprocessing and rolling smoothing processing to obtain a smoothing power and smoothing rotating speed sequence, determining a first key time point based on the smoothing power sequence, carrying out segmented backtracking and descending trend detection on the smoothing power sequence, and determining a second key time point. And carrying out descending track detection on the smooth rotating speed sequence, determining a third key time point, backtracking the smooth rotating speed sequence, determining a fourth key time point, determining a fifth key time point based on the original rotating speed, and judging whether a mechanical overspeed test is carried out or not. According to the invention, problems in the prior art are solved, and reliable and automatic identification and accurate event definition of the mechanical overspeed test are realized.

The present application relates to the technical field of lesion recognition, in particular to an automatic lesion recognition ultrasonic system for real-time monitoring, which comprises a delay path discrimination module, a gray level trend detection module, a texture feature screening module, a feature fusion sorting module and a region highlight labeling module. Based on continuous ultrasonic frames, the collected deep tissue echo path is analyzed, and the echo arrival time of each pixel point in the continuous frame is detected. The present application supports multi-type data fusion judgment through a comprehensive judgment process supported by multi-dimensional parameter collaborative screening, penetration behavior, gray level trend and texture aggregation. The regional abnormal priority sorting mode improves the hierarchy of lesion feature discrimination, provides partition directional recognition for local structural abnormalities and early micro-variation, and converts the feature judgment result into a high confidence region label by a weight aggregation method. The image output process automatically completes the real-time visual presentation of the high-risk area, improving the clarity and pertinence of the lesion region presentation.

The application discloses a cutterhead mud cake detection method and device, electronic equipment and a storage medium, and belongs to the technical field of tunnel construction. The detection method comprises the following steps: reading historical tunneling data from a PLC controller and an industrial computer of a shield tunneling machine; training a parameter trend detection model by using mud cake related parameters in the historical tunneling data; the parameter trend detection model comprises a one-dimensional convolution layer, an LSTM layer and a full connection layer; obtaining mud cake related parameters of the shield tunneling machine in a target time period, inputting the mud cake related parameters in the target time period into the parameter trend detection model, and obtaining a change trend of the mud cake related parameters in the target time period; judging whether the change trend of the mud cake related parameters in the target time period conforms to cutterhead mud cake characteristics; if yes, it is determined that the shield tunneling machine is in a cutterhead mud cake state; and if no, it is determined that the shield tunneling machine is not in the cutterhead mud cake state. The application can improve the accuracy of detecting cutterhead mud cake.

The abnormality analysis text automatic generation method, system and equipment of DHI interpretation report belong to the cross field of breeding technology and data text generation technology. In order to solve the problems of large workload and low efficiency of manually writing abnormality analysis text in the current DHI interpretation report, the DHI key performance index data is first acquired, the position of the data value of this month in the corresponding abnormality degree type array is located, the performance index name and the corresponding abnormality degree value are directly spliced, and the description text of the performance index static abnormality is obtained. Meanwhile, the global movement, local movement and standardization processing are carried out on the data of this month and the historical data, a single-layer bidirectional GRU network is used as an encoder for processing, the hidden states of all time steps and the hidden state of the decoder t-1 step are processed by using an attention layer, and an LSTM long short-term memory network is used as a decoder to generate the description text of the performance index dynamic trend detection.

The invention relates to the technical field of speech recognition and audio signal processing, in particular to an ultra-low power consumption speech wake-up system with adaptive threshold adjustment capability, which comprises an input sound processing module and an energy trend detection module for monitoring and receiving an audio signal x (t) from a microphone and outputting an envelope signal En (t); the adaptive control module updates the gain Gain of the programmable amplifier according to the envelope signal En (t); the programmable amplifier adopts the updated gain Gain to amplify the audio signal x (t) to obtain a signal x '' (t), and the signal x '' (t) passes through a band-pass filter to obtain a signal V 'BPF (t); the threshold judgment and wake-up output module adaptively adjusts a wake-up threshold Threshold according to the envelope signal En (t), and outputs a wake-up mark digital signal; the problem that an existing fixed sound pressure threshold value scheme cannot automatically adjust the wake-up threshold according to environmental noise changes is solved.

Methods and systems for media trend detection and maintenance are provided herein. A set of media items each having common media characteristics is identified. A set of pose values is determined for each respective media item of the set of media items. Each pose value is associated with a particular predefined pose for objects depicted by the set of media items. A set of distance scores is calculated. Each distance score represents a distance between the respective set of pose values determined for a media item and a respective set of pose values determined for an additional media item. A coherence score is determined for the set of media items based on the calculated set of distance scores. Responsive to a determination that the coherence score satisfies one or more coherence criteria, a determination is made that the set of media items corresponds to a media trend of a platform.

The invention provides a measurement data change trend detection method and system and a computer storage medium. The measurement data change trend detection method comprises the steps that historical measurement data and new measurement data are integrated; sequentially selecting a plurality of groups of preset quantity of data from the integrated historical measurement data and the new measurement data according to the time sequence, and judging whether a second extreme value in each group of preset quantity of data is behind the first extreme value or not; if it is judged that the second extreme value in the preset number of data of the current group is behind the first extreme value, intercepting interval data from the first extreme value to the second extreme value in the preset number of data of the current group; and combining all the interval data according to the time sequence, and judging whether the change trend of the combined interval data is abnormal or not. According to the technical scheme, the change trend of the measurement data can be effectively detected.

The invention provides an etching end point detection method, a semiconductor structure and a manufacturing method thereof, and the etching end point detection method can accurately determine an etching end point based on the direct detection of the concentration of a first reaction product in an etching process. Compared with trend detection of the concentration of the first reaction product through a spectrum mode and the like, the accuracy of the etching end point determined in the method is higher. Meanwhile, according to the manufacturing method of the semiconductor structure based on the etching end point detection method, the etching end point can be automatically detected in the etching process of the wafer to be etched, etching is stopped, excessive etching or insufficient etching is avoided, the accuracy of the size of the formed semiconductor structure and the stability of the etching process are guaranteed, and the yield is improved. Therefore, the manufacturing yield of the semiconductor structure is improved.

This invention discloses a method for analyzing soil pollution change trends, comprising: acquiring a dataset of soil monitoring points at multiple time points within a target area; delineating core areas and time periods where pollutants in the soil exhibit significant upward or downward trends and spatially form a pattern; spatiotemporally matching the core areas and time periods with human activity factors and natural factors, constructing spatial statistical models, and obtaining the contribution rates of human activity factors and natural factors to the pollution change trend; this invention effectively integrates the inconsistency problem of spatiotemporal dynamic monitoring data, overcomes the data comparability barrier caused by changes in monitoring points, and realizes a closed-loop analysis from trend detection to cause analysis, significantly improving the accuracy and practicality of soil pollution source tracing and risk management.

The invention relates to the technical field of lesion recognition, in particular to an automatic lesion recognition ultrasonic system for real-time monitoring, which comprises a delay path judgment module, a gray scale trend detection module, a texture feature screening module, a feature fusion sorting module and a region highlight labeling module. The acquired deep tissue echo path is analyzed, and the echo arrival time of each pixel point in continuous frames is detected; according to the method, multi-type data fusion judgment is supported through a comprehensive judgment process of multi-dimensional parameter collaborative screening, penetration behavior, gray trend and texture aggregation, hierarchy of lesion feature judgment is improved through a regional anomaly priority sorting mode, and partition directional recognition is provided for local structure anomaly and early tiny variation; the weight aggregation method converts a feature judgment result into high-confidence region labeling, real-time visual presentation of a high-risk region is automatically completed in the image output process, and the presentation definition and pertinence of a lesion region are improved.

This invention discloses a clean air leak detection system and method based on the Internet of Things (IoT), relating to the field of industrial process monitoring. The method includes: preprocessing the acquired raw monitoring signal to obtain a standardized signal; performing robust statistical analysis on the standardized signal based on a sliding window to obtain the time-varying baseline center and the time-varying baseline fluctuation intensity; correcting the standardized signal according to the time-varying baseline center to obtain a residual signal; performing wavelet multi-resolution decomposition on the residual signal to obtain approximation coefficients and detail coefficients distributed at different scales; performing threshold shrinkage processing on the detail coefficients and reconstructing a trend enhancement signal using the processed detail coefficients and approximation coefficients; adaptively setting detection parameters based on the time-varying baseline fluctuation intensity, and using a sequential cumulative sum algorithm to detect persistent shift trends in the trend signal. Through adaptive baseline estimation, wavelet denoising, and sequential cumulative sum detection, high sensitivity and low false alarm detection of weak leak trends are achieved.

The invention discloses an access method and system on an industrial data link, and relates to the field of industrial data processing, and the method comprises the steps: collecting the time sequence monitoring data of industrial equipment, deploying a trend detector at the edge side, and dynamically adjusting the anchoring frequency on the link according to the data sudden change intensity and the abnormal confidence; when a transient event is captured, an event snapshot containing a time window and an associated point location is generated, the event snapshot is linked preferentially through an independent channel, and the event snapshot falls back to a conventional anchoring rhythm after abnormities subside. When historical data is archived in a roll-by-roll mode, connection records containing inter-roll associated indexes are generated in roll switching to be linked, a cross-roll index directory corresponding to a hash root on the chain is maintained, and an anchoring time period window on the chain is inquired before roll cutting of an archiving system so as to align a boundary. According to the method, the problems of transient abnormal tracing blind areas and cross-volume chain breakage caused by existing fixed anchoring are solved, the data tracing continuity and the key event capturing capability are improved, and reliable support is provided for industrial data auditing and fault tracing.

The present application relates to the technical field of tunnel disaster identification, and particularly relates to a tunnel portal ice disaster identification and prediction method based on multi-modal data fusion technology. The technical scheme comprises multi-source data acquisition, heterogeneous data processing, feature-level fusion, disaster identification, spatio-temporal prediction and dynamic early warning. The present application collects tunnel portal temperature, spatial structure and environmental parameters by deploying multi-modal sensors, and realizes ice layer distribution identification, icing trend detection and spatio-temporal prediction by intelligent processing and multi-level feature fusion using a deep network. The present application dynamically activates the ice-melting device and links the vehicle warning by combining the hierarchical early warning mechanism, forms a closed loop of perception, analysis, prediction, disposal and calibration, significantly improves the accuracy of ice disaster detection, the foresight of prediction and the intelligence of disposal, and guarantees the safety of tunnel traffic and the long-term robustness of the system.

This invention discloses a method, apparatus, electronic device, and storage medium for processing multipath effects in a Time-of-Flight (TOF) sensor. After acquiring the elevator door closing distance and the initial TOF image collected by the TOF sensor, the method identifies the target image region affected by the multipath effect from the initial TOF image based on the door closing distance. The target image region is then masked from the initial TOF image to obtain a target TOF image. Trend detection is performed based on the target TOF image. This allows for the identification of the target image region affected by the multipath effect caused by the door frame edge or door panel edge from the TOF image at different stages of elevator door closing, based on the door closing distance. Furthermore, the target image region is masked from the TOF image, thus excluding the image region with multipath effect from trend detection. This avoids the influence of multipath effect on trend detection, improves the accuracy of trend detection, and makes controlling the elevator door closing or opening more safe and reliable based on the trend detection results.

The invention belongs to the technical field of intelligent fault detection of an airport production operation system, and discloses an airport production operation system fault identification method and device based on machine learning. The method comprises the following steps: preprocessing collected service request information of a production operation system recorded in a system log; sudden change detection and trend detection are carried out on the airport production operation system faults, and sudden change abnormity of the airport production operation system faults and trend abnormity of the airport production operation system faults are detected respectively; the method is applied to airport production operation scenes, and the recognition rate, the false alarm rate and the response time index are counted respectively. According to the method, the identification rate of the abnormal events of the index mutation class is 98.7%, and the false alarm rate is lt; and the recognition rate is 91.2% within 7 days for continuous abnormity of the trend. The application effect is obvious, the abnormal state of the system can be accurately detected in advance, and timely processing is carried out to avoid expansion of the fault range.

The application discloses a real-time anomaly detection and intelligent early warning system and method based on artificial intelligence, relates to the technical field of artificial intelligence and data security, and comprises the following steps: acquiring historical data assets and constructing a tensor potential function; guiding the migration of generated detector particles through the tensor gradient of the tensor potential function; filtering the migrated detector particles through negative selection to generate an initial detector, performing cloning on the initial detector to generate a cloned detector, and migrating to a potential abnormal area based on the tensor gradient; when the cloned detector stably resides in the potential abnormal area, recording the evolution track of the cloned detector and constructing a trend memory unit; generating a trend detector according to the trend memory unit and migrating to an area where the detector particles are insufficient based on the tensor gradient; and performing response matching on the to-be-detected data assets, the cloned detector and the trend detector to complete anomaly detection and early warning. The application ensures high detection accuracy and early warning response rate in a complex data environment.

The application provides a cotton picker picking head clogging trend detection and early warning method and system based on double-path feature fusion and prediction reconstruction, comprising obtaining picking head vibration signals and cutting into vibration windows; statistical physical features and original vibration sequence deep features of each window are extracted and window-level fusion is performed to construct a time series context feature sequence; the time series context feature sequence is input into a time series prediction model to obtain prediction target features and hidden states of a future target window; hidden space distribution parameters are generated based on the hidden states and latent variables are determined, and reconstruction target features are obtained through a reconstruction constraint model; a window anomaly score is constructed according to a prediction error, a reconstruction error and a hidden space divergence, and an initial healthy window is combined to establish an intra-source adaptive baseline; when the anomaly score exceeds a warning threshold and the continuous window condition is met, a warning result is output. The application considers feature interpretability and deep representation ability, realizes high-precision early warning of clogging trend anomalies, and is self-adaptive, strong and highly stable.

In general, devices, systems, and techniques are described for determining periodicity metrics for electrical signals sensed from a patient. In one example, a system includes processing circuitry configured to receive, from sensing circuitry, a plurality of electric signals from a patient sensed over a time duration, determine characteristic values for the plurality of bioelectric signals, and determine, based on the characteristic values, a periodicity metric indicative of one or more repeating periods for the plurality of bioelectric signals. The processing circuitry can then control delivery of electrical stimulation therapy based on the periodicity metric.

The application discloses an industrial data chain access method and system, and relates to the field of industrial data processing.The method comprises the following steps: collecting time sequence monitoring data of industrial equipment, deploying a trend detector on the edge side, dynamically adjusting the anchor frequency on the chain according to the data mutation intensity and abnormal confidence, generating an event snapshot containing a time window and associated point positions when capturing a transient event, preferentially uploading the event snapshot to the chain through an independent channel, and falling back to the normal anchor rhythm after the abnormality subsides.When archiving historical data, a link record containing an inter-volume association index is uploaded to the chain during volume switching, a cross-volume index directory corresponding to the hash root on the chain is maintained, and the anchor time period window on the chain is queried before the archiving system switches the volume to align the boundary.The application solves the problems of transient abnormality tracing blind area and cross-volume disconnection caused by the existing fixed anchor, improves the data tracing continuity and key event capturing capability, and provides reliable support for industrial data auditing and fault tracing.

The system according to this embodiment aims to select news articles based on the user's interests and browsing history, and to provide highly reliable information. [Solution] The system according to the embodiment comprises a selection unit, a summarization unit, an analysis unit, and a countermeasure unit. The selection unit selects news articles based on the user's interests and browsing history. The summarization unit automatically summarizes the articles selected by the selection unit. The analysis unit analyzes trends based on the articles summarized by the summarization unit. The countermeasure unit detects clickbait based on the trends analyzed by the analysis unit.

This invention discloses a test monitoring method and system for a combined transformer test power supply, comprising: testing and monitoring the target test power supply to obtain power supply test monitoring information; preprocessing the power supply test monitoring information to obtain preprocessing result information; constructing an anomaly detection model, inputting the preprocessing result information to detect abnormal operating data occurring during power supply testing, and obtaining anomaly detection information; performing trend analysis based on the anomaly detection information to predict future changes in power supply test monitoring data and detect abnormal trends, and obtaining anomaly trend detection information; extracting anomaly features based on the anomaly trend detection information, pre-setting risk assessment rules, and performing risk assessment based on the extracted anomaly features, and obtaining risk assessment information; determining the existence of test safety risks based on the risk assessment information, and if so, using a fuzzy controller to generate a control scheme for control to ensure the safety and reliability of power supply testing.