68 results about "Trend detection" patented technology

The invention relates to the technical field of tunnel disaster identification, in particular to a tunnel portal icing disaster identification and prediction method based on a multi-modal data fusion technology. According to the technical scheme, the method comprises the steps of multi-source data acquisition, heterogeneous data processing, feature level fusion, disaster recognition, space-time prediction and dynamic early warning. According to the invention, a multi-modal sensor is deployed to collect tunnel portal temperature, space structure and environmental parameters, intelligent processing and multi-stage feature fusion are carried out, ice layer distribution identification, icing trend detection and time-space prediction are realized by using a deep network, an ice melting device is dynamically activated in combination with a graded early warning mechanism, and vehicle early warning is linked. A sensing, analysis, prediction, disposal and calibration closed loop is formed, the accuracy of ice coagulation disaster detection, the prospective performance of prediction and the intelligence of disposal are remarkably improved, and the tunnel traffic safety and the long-term robustness of the system are guaranteed.

The invention discloses an industrial control network threat trend detection method and system based on time sequence characteristics, and particularly relates to the technical field of industrial control networks. The method comprises the following steps: dynamically adjusting an existing threat detection model to improve the adaptability by introducing time series data after new equipment or system upgrading, inputting new data into an original model, and evaluating the model performance according to a reaction speed; if the performance is reduced, judging the characteristic consistency of the model by analyzing the drifting of the time series data characteristics; then, the stability of the model on different data sets is evaluated through hybrid verification; according to the evaluation results, the adaptability of the model is judged, the model is divided into a high-adaptability model or a low-adaptability model, for the low-adaptability model, a threshold value is dynamically adjusted, false alarm frequency abnormity is further analyzed and false alarms are filtered through a secondary detection mechanism, the adaptability of the model in data change is effectively improved, the false alarm risk is reduced, and the accuracy of the data change is improved. And the safety and the reliability of the system are enhanced.

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 relates to the technical field of health data processing, in particular to a cross-device health data fusion method, which comprises the following steps of monitoring a device connection state, identifying data missing and resending, calibrating multi-device time and performing data difference, calculating a device stability score to adjust confidence fusion data, extracting periodic health parameters and analyzing a change trend. And detecting the factor activation state and combining the sensitivity to obtain a risk score, and outputting a health processing result. According to the method, through combination of equipment connection state identification and data caching progress calculation, the precision of breakpoint data recovery is enhanced, a timestamp alignment and interpolation correction mechanism is adopted, continuous compensation of multi-source data is realized, the data weight is adjusted by using a stability score, the credibility of fused data is improved, and the reliability of the fused data is improved. In combination with behavior characteristic trend extraction and risk factor sensitivity determination, the accuracy and individualized distinguishing ability of chronic disease risk identification are optimized, and the individualized health management and disease prevention ability is enhanced.

An on-board insect monitoring system, comprising insect luring and capturing means, a digital camera, cleaning means, and a processor configured to acquire images or video from the camera, recognize at least one insect species; count insects of the species, evaluate an insect density, provide an indication corresponding to the recognition, the count and / or the density, and actuate the cleaning means in response to the density. A method for monitoring and predicting insect populations, comprising providing insect monitoring stations for collecting data comprising a presence of an insect species, a number of insects of the species, and / or and a total number of insects, providing the data to a server, retrieving previously received data, determining, a change in the data, ascribing, to each station, a predicted value for the data based on the changes and the station locations and providing the data and / or the predicted value to a user.

The invention discloses a test monitoring method and system for a combined transformer test power supply, and the method comprises the steps: carrying out the test monitoring of a target test power supply, obtaining power supply test monitoring information, carrying out the data preprocessing of the power supply test monitoring information, and obtaining the preprocessing result information; an anomaly detection model is constructed, preprocessing result information is input to detect operation anomaly data occurring during power supply testing, and anomaly detection information is obtained; performing trend analysis based on the abnormal detection information, predicting the change of the power supply test monitoring data at the future moment, and detecting an abnormal trend to obtain abnormal trend detection information; extracting abnormal features based on the abnormal trend detection information, presetting a risk assessment rule, and performing risk assessment according to the extracted abnormal features to obtain risk assessment information; according to the risk assessment information, it is judged that a test safety risk exists, and if yes, a fuzzy controller is used for generating a regulation and control scheme for regulation and control, so that the safety and reliability of power supply testing are guaranteed.

The invention discloses a control method and system for a rotatable display screen, and particularly relates to the technical field of display screen control. Precise rotation control is realized through module initialization and physical zero calibration after system power-on and in combination with angle error calculation and feedback control; a zero offset trend detection and dynamic compensation algorithm is particularly introduced, the offset of the reference position of the encoder is effectively corrected, the rotation precision and the system stability in a multi-task control environment are ensured, and the use reliability of equipment and the user experience are remarkably improved through real-time feedback, error closed-loop adjustment and state locking output.

A heterogeneous industrial equipment-oriented adaptive data fusion and decision optimization system relates to the technical field of industrial automation and information, and comprises a cloud, and the cloud is in communication connection with a data acquisition module, a protocol labeling module, a protocol self-consistent module, a detection decision module, a fast response compensation module and a critical trend detection module; the data acquisition module is used for acquiring multi-source type data of each heterogeneous industrial device, and the protocol labeling module is used for constructing an industrial connection topological graph and performing initial communication protocol labeling on the industrial connection topological graph; the protocol self-consistent module is used for carrying out interactive feature analysis on each node in the industrial connection topological graph and carrying out automatic protocol matching on protocol abnormal nodes; the detection decision module detects the multi-source type data of each node in real time, and selects the rapid response compensation module to execute the rapid response compensation operation or the critical trend detection module to execute the critical trend detection operation according to a monitoring result, so that the production efficiency is remarkably improved, and the product quality is ensured.

The invention discloses a software testing system based on virtual reality, which comprises a data acquisition module, a testing module and an output module, and is characterized in that the testing module is used for deploying a running exercise amount target of a user on the current day; the data acquisition module, the test module and the output module are in communication connection with one another, user picture information is called and analyzed within different time through the head inclination angle detection sub-module, and a favorable basis is provided for the test system to monitor the motion state of a user; a foot force application area detection sub-module is used for acquiring a specific foot force application area of the user in a monitoring time period, and judging whether the current motion state of the user is stable or not; through the step number change trend detection sub-module, the step number increase condition of the user in the monitoring time period is monitored, the humanization degree of the treadmill is greatly improved, the daily experience of the user using the treadmill is improved, and the treadmill has the advantages of being high in humanization degree and high in motion parameter monitoring precision.

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.

System and methods provide driver performance rating and driving coaching feedback and make incident and exceedance predictions. Driving event data including data spanning multiple separate driving trips is analyzed using a trend detection model to generate a trend detection result that is used to determine a driver performance rating. The trend detection model may be a functional regression model, a linear fit model and / or a polynomial fit model. Driver coaching signals representative of driving instructions are generated based results of the trend detection model applied to the driving event data. Driving incident predictions are made based on the results of the trend detection model applied to the driving event data to a predetermined threshold.

The invention belongs to the technical field of stereoscopic vision, and discloses a stereoscopic matching method based on unsupervised online learning. The method is realized through a stereo matching module based on a multi-level cost body, a non-occlusion region confidence estimation module and a Mann-Kendall trend detection module. Compared with the prior art, the method has a better stereo matching effect. The model provided by the invention has the best result in both the initial stage and the later stage, and due to the good generalization of the model, the model can always keep a relatively accurate prediction result in a sheltered region. The model has a good prediction result in the occlusion area due to high generalization, and the tendency of parameter error update is relieved due to the fact that the self-adaptive adjustment strategy provided by the invention eliminates the influence of error loss of most of the occlusion areas, so that the network can maintain the optimal performance all the time.

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 invention relates to a method for achieving load prediction based on federated learning of multi-party samples, and the method comprises the steps: S1, carrying out the local model training of multi-party samples of federated learning; s2: federated learning model aggregation: specially designing an aggregation model for a load prediction scene, wherein the construction of the aggregation model needs to carry out analysis and trend detection on a prediction result of a global model; s3, federal learning privacy data protection: a differential privacy protection technology is adopted, the technology protects local data by adding noise to data or training model output, original privacy data is prevented from being obtained through reverse calculation, and a noise adding mode should be determined according to data characteristics and privacy protection requirements; the load prediction accuracy is high, and a more reliable result can be provided no matter short-term load fluctuation or long-term load trend prediction. The model training efficiency is high, and the training efficiency of the whole federated learning load prediction model is improved, so that support can be provided for load prediction of the power distribution network more timely.

The invention discloses a time series data anomaly detection method and device, electronic equipment and a computer program product. The method comprises the following steps: acquiring a to-be-analyzed time sequence queue comprising a plurality of to-be-analyzed time sequence data; sliding a preset sliding window on the to-be-analyzed time sequence queue to obtain window data corresponding to the preset sliding window after each sliding; detecting a short-time trend of the window data according to a plurality of pieces of window data in the first window sequence; under the condition that the short-time trend shows that trend change occurs in the multiple pieces of window data in the first window sequence, the long-time trend of the window data is detected according to the multiple pieces of window data in a second window sequence, and the length of the second window sequence is larger than that of the second window sequence; and generating abnormal early warning information under the condition that the long-time trend shows that trend change occurs in the multiple pieces of window data in the second window sequence. The technical problem that in the prior art, the accuracy rate of an anomaly detection result obtained through trend detection is low is solved.

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.

In various embodiments, systems, methods, and techniques are disclosed for generating a collection of clusters of related data from a seed. Seeds may be generated based on seed generation strategies or rules. Clusters may be generated by, for example, retrieving a seed, adding the seed to a first cluster, retrieving a clustering strategy or rules, and adding related data and / or data entities to the cluster based on the clustering strategy. Various cluster scores may be generated based on attributes of data in a given cluster. Further, cluster metascores may be generated based on various cluster scores associated with a cluster. Clusters may be ranked based on cluster metascores. Various embodiments may enable an analyst to discover various insights related to data clusters, and may be applicable to various tasks including, for example, tax fraud detection, beaconing malware detection, malware user-agent detection, and / or activity trend detection, among various others.

The invention relates to a production line monitoring method and device, equipment, a storage medium and a program product. The method comprises the following steps: grouping production line data of a target process in a battery production line to obtain a plurality of data groups; for each data group, determining at least one initial change trend according to a plurality of continuous production line data with consistent change trends in the group; performing fitting processing on the plurality of production line data corresponding to each preliminary change trend, and determining at least one target change trend according to a fitting result; wherein the target change trend is used for monitoring the battery production line. According to the invention, the accuracy of trend detection can be improved, so that the problem of missing report is reduced.

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 belongs to the technical field of enterprise management, and particularly relates to an evaluation method and system for an enterprise green total factor productivity influence mechanism, and the method comprises the steps: obtaining related data before and after the optimization of an enterprise business environment, constructing a parallel trend detection model, carrying out the empirical analysis, and extracting target variable data. On the basis, a business environment influence regression model and a regulation effect regression model are constructed, and an influence mechanism of the enterprise green total factor productivity is determined through reference regression. And the specific adjustment effect of the enterprise digital transformation between the business environment optimization and the enterprise green total factor productivity can be clarified. Therefore, powerful support is provided for an enterprise to formulate a targeted optimization strategy, and the enterprise is helped to better utilize digital transformation and business environment optimization to improve the green total factor productivity. And finally, through defining the adjustment effect of enterprise digital transformation, the method helps the enterprise to break through the dilemma that the current influence mechanism is unknown, and promotes the sustainable development of the enterprise.

An adaptive data fusion and decision optimization system for heterogeneous industrial equipment, which relates to the field of industrial automation and information technology, includes a cloud. The cloud is communicatively connected to a data acquisition module, a protocol annotation module, a protocol self-consistency module, a detection and decision module, a rapid response compensation module, and a critical trend detection module. The data acquisition module is used to collect multi-source type data of each heterogeneous industrial equipment. The protocol annotation module is used to construct an industrial connection topology diagram and perform initial communication protocol annotation on the industrial connection topology diagram. The protocol self-consistency module is used to analyze the interaction characteristics of each node in the industrial connection topology diagram and automatically match the protocol for protocol-abnormal nodes. The detection and decision module performs real-time detection on the multi-source type data of each node, and according to the monitoring results, selects the rapid response compensation module to perform rapid response compensation operations or the critical trend detection module to perform critical trend detection operations, significantly improving production efficiency and ensuring product quality.

The present invention relates to the field of health data processing technology, specifically to a cross-device health data fusion method, including the following steps: monitoring device connection status, identifying data loss and reissuing it, calibrating multi-device time and performing data interpolation, calculating device stability score to adjust confidence fusion data, extracting periodic health parameters and analyzing change trends, detecting factor activation status and combining sensitivity to derive risk scores, and outputting health processing results. In the present invention, by combining device connection status identification with data cache progress calculation, the accuracy of breakpoint data recovery is enhanced, timestamp alignment and interpolation correction mechanism are adopted to achieve continuity compensation of multi-source data, and stability score is used to adjust data weights, thereby improving the credibility of fused data. By combining behavioral feature trend extraction with risk factor sensitivity determination, the accuracy of chronic disease risk identification and individual differentiation capabilities are optimized, and the ability of personalized health management and disease prevention is enhanced.

The present application relates to the field of data processing, and specifically provides a road intelligent lighting control method and system based on vehicle-road-cloud collaboration. The method identifies vehicle flow based on a road time-series monitoring data set containing a target vehicle flow intensity, and obtains time-series distribution information. Based on this, the target vehicle flow intensity is subjected to evolution trend detection, which can reduce data computing consumption. Feature extraction is performed on the time-series distribution information of the target vehicle flow intensity in each road monitoring data sequence to obtain a time-series distribution sub-implicit representation sequence, and unqualified time-series distribution information can be deleted. The time-series distribution information corresponding to the target vehicle flow intensity in each road monitoring data sequence is unified to obtain a time-series distribution sub-implicit representation sequence, and generalization is improved. In addition, implicit representation based on the time order between sequences increases the accuracy of feature representation.

The invention relates to the technical field of order management, in particular to an AI-based game order management system which comprises a dependency recognition module, an affiliation matching module, a frequency curve module, a trend detection module and an order locking module. According to the method, the directivity relation between the fields is expressed by constructing the field dependency path diagram, accurate description of order structure features is achieved, the type attribution judgment accuracy is improved, the structure complexity quantification capability is enhanced through dependency edge number interval mapping, and the type distinguishing effect is enhanced; the combination frequency trend of the role level and the recharge amount in the period reveals the behavior active stage, the frequency amplification quantifies the behavior change intensity, and the device type newly-added ratio and frequency trend linkage analysis can identify the migration characteristics of the login path, capture the non-continuous behavior evolution, extract the user ID based on the field combination and evaluate the repetition rate. The identification coverage range and the response precision of potential abnormal aggregation behaviors are improved, and the risk identification capability in a complex order environment is enhanced.

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.

The invention discloses a production line visual management platform based on dynamic task data driving, and relates to the technical field of intelligent manufacturing and industrial informatization, and the platform comprises a dynamic task data collection module which is used for collecting the operation state data of production line stations, equipment, personnel and materials in real time; the task evolution modeling module is used for constructing a task evolution process according to the collected data; the task granularity self-adaptive refining module is used for dynamically adjusting the task management granularity according to the task state change; the abnormal fluctuation trend detection module is used for detecting abnormal fluctuation in the task state data; the visual management module is used for generating a production line visual interface based on the task evolution and the anomaly detection result; and the scheduling decision linkage module is used for optimizing a production line scheduling strategy according to the anomaly detection result and the task state. The problems of static data updating, task granularity fixing and anomaly response lagging existing in an existing production line visual management platform are solved.

A method for automated routing of pictures taken on mobile electronic devices to a digital picture frame including a camera integrated with the frame, and a network connection module allowing the frame for direct contact and upload of photos from electronic devices or from photo collections of community members. Clustering photos by content is used to improve display and to respond to photo viewer desires. Trends or patterns can be detected from the photo collections and that information used for various purposes beyond photo display.

The invention relates to the technical field of seismology, and particularly discloses a K value self-pickup method based on MK trend detection, which comprises the following steps of: preprocessing seismic oscillation data; performing curve feature recognition on the seismic oscillation Fourier spectrum by using an MK trend detection algorithm; picking up starting and ending frequencies of a seismic oscillation high-frequency descending section according to the Fourier spectrum ascending and descending characteristic curve; and extracting a descending frequency band according to the start-stop frequency, and performing fitting calculation to obtain a K value. According to the method, the value of the seismic oscillation high-frequency attenuation parameter K is calculated by extracting the curve rising and falling characteristics of the seismic oscillation Fourier spectrum and then identifying the starting and ending frequencies, so that the precision of picking up the starting and ending frequencies of the descending section of the Fourier spectrum can be remarkably improved, and batch calculation of the seismic oscillation high-frequency attenuation parameters is realized; the method has the characteristics of high flexibility, high accuracy, high efficiency, simplicity in operation and the like.

The invention discloses a real-time anomaly detection and intelligent early warning system and method based on artificial intelligence, and relates to the technical field of artificial intelligence and data security, and the method comprises the steps: obtaining historical data assets, and constructing a tensor potential energy function; guiding the generated detector particles to migrate through the tensor gradient of the tensor potential energy function; negative selection is carried out on the migrated detector particles, an initial detector is generated, cloning is carried out on the initial detector, a clone detector is generated, and the detector is migrated to a potential abnormal region based on the tensor gradient; when the clone detector stably resides in the potential abnormal region, recording an evolution trajectory of the clone detector, and constructing a trend memory unit; according to a trend memory unit, generating a trend detector and migrating to a detector particle insufficient coverage area based on a tensor gradient; and performing response matching on the to-be-detected data assets, the clone detector and the trend detector to complete anomaly detection early warning. And high detection precision and early warning response rate are kept in a complex data environment.