280 results about "FARR assay" patented technology

The invention relates to the technical field of atomization rate control, in particular to an intelligent atomization compliance monitoring method and system for children. The method comprises the following steps: firstly, acquiring expiration flow velocity, inspiration flow velocity and multi-direction motion acceleration time sequence data of a child in a historical monitoring period, and reflecting breathing and behavior action conditions; dynamically analyzing the numerical characteristics of the expiration flow velocity and the inspiration flow velocity to divide a respiratory cycle, and analyzing the cycle length, the inspiration flow velocity cumulant and the like to construct a respiratory characteristic vector; screening a target monitoring period based on the breathing feature vector and the period number similarity; further analyzing the motion acceleration change and accumulation characteristics in the target monitoring period, and quantifying the behavior compliance expression degree; the behavior compliance expression degree and the breathing feature vector are deeply fused, a'breathing state-behavior cooperation 'joint evaluation model is established, the breathing and behavior states of children are dynamically recognized, the medicine atomization rate is accurately adjusted, and effective deposition of lung medicine is improved.

The invention discloses a digital twin high-voltage motor-based strong current operation state monitoring and maintenance system, which comprises the following steps: collecting current, temperature rise and vibration signals in motor operation, carrying out high-precision fitting processing on the signals by using a Chebyshev polynomial approximation algorithm, carrying out trend offset detection on a fitting result based on a CUSUM algorithm, and carrying out high-precision fitting processing on the fitting result based on a CUSUM algorithm; therefore, tiny abnormal changes are identified. And meanwhile, a prediction signal is generated in combination with a digital twinborn model, residual calculation is performed on the prediction signal and the fitting signal, fault features are further analyzed, and early warning information is output. Real-time sensing, trend judgment, fault prediction and intelligent early warning of the running state of the high-voltage motor are achieved, and the system is suitable for motor monitoring and maintenance in an industrial scene.

The invention relates to the technical field of voice interaction, in particular to a dialogue type AI low-delay response control method and system based on semantic analysis. The method comprises the following steps: firstly, generating a driving intensity correlation factor of a current time window according to the change of CAN bus data; further monitoring the output of the NLU, analyzing the acoustic intonation raising characteristics, and obtaining an interaction suppression coefficient in combination with the driving intensity correlation factor; further acquiring a time pressure coefficient based on the mute duration after the user stops sounding; further comparing the interaction suppression coefficient and the time pressure coefficient of the current time window to obtain a response judgment value; further analyzing the change trend of the interaction inhibition coefficient, and updating an environment improvement flag bit; and finally, according to the driving intensity correlation factor, the environment improvement flag bit and the response decision value of the current time window, carrying out multi-level response decision control, and carrying out selective reset of an interaction state, thereby solving the problems of wrong truncation and response delay of voice interaction under dynamic driving.

The invention discloses a fault diagnosis method for a series energy storage lithium battery pack under a charging condition, and the method comprises the following steps: (1), monitoring and collecting the single voltage and temperature of each cell in real time, and uploading the single voltage and temperature; (2) taking the single voltage of the battery cell as the characteristic of the battery cell and expanding; (3) expanding the characteristics of the battery cells, and introducing a voltage correlation coefficient between the battery cells as a new characteristic dimension; (4) calculating a correlation coefficient change rate of the current time window and the previous time window as a new feature; (5) calculating a Z-Score score of the information entropy as an expansion feature; (6) determining the characteristic vectors of the single cells to obtain a final characteristic matrix; (7) the feature matrix is put into an isolated forest algorithm for abnormal point identification; and (8) further analyzing and screening the battery cell data points marked as abnormal, and determining an abnormal reason and a fault type. The method does not need battery modeling, is suitable for various different types of battery cells, and is simple, convenient and rapid.

The invention belongs to the technical field of intelligent terminal mainboard testing, and discloses an intelligent terminal mainboard function verification system based on multi-protocol compatibility. Comprising a data acquisition module used for performing benchmark testing on each communication protocol supported by the intelligent terminal mainboard to obtain a benchmark data set of the intelligent terminal mainboard; the analysis and sequence generation module is used for acquiring a resource consumption portrait of the intelligent terminal mainboard; generating a concurrent test instruction sequence based on a pre-constructed test sequence generation engine; the concurrent test and diagnosis module is used for acquiring a concurrent test operation log by executing the concurrent test instruction sequence; identifying a causal relationship between a protocol performance degradation event and a resource state abnormal event based on the concurrent test operation log, and forming a resource protocol conflict event record; and the diagnosis and report generation module is used for acquiring diagnosis results of the resource protocol conflict events and further analyzing and generating a comprehensive verification report, so that a clear decision basis is provided.

The subject of the invention is the method of adaptive creating network traffic filtering rules on a network device that autonomously detects anomalies and adaptively mitigates volumetric (DDoS) attacks on at least one network device (4) based on actual network flows (3) and after separating them into isolated packet flows (9), recognizes potentially harmful network flows, and then configures or tunes the network filters (19) and packet policing means (17), wherein filtering rules (18) can be propagated to other network devices (27) and selects for further analysis the isolated packet flows (9) associated with at least one configured or tuned network filter (19).

The invention provides a method for constructing a geoelectric structure prediction model based on time-frequency electromagnetism, and belongs to the field of oil exploration. The method comprises the following steps: firstly, establishing an initial reservoir model based on a model-driven geophysical inversion process; then geophysical data and a corresponding reservoir model data set are collected, classified and standardized, and construction of the data set is completed; establishing a reservoir geoelectric structure prediction model based on the deep learning model; and finally training the reservoir geoelectric structure prediction model by using the data set to obtain an optimized reservoir geoelectric structure prediction model. Wherein the reservoir comprises an ultra-deep carbonate rock reservoir; the invention further provides a method for constructing the model for predicting the geoelectric structure of the ultra-deep carbonate reservoir. Compared with the prior art, according to the geoelectric structure prediction model constructed by the method, when observation data is input, an inversion result can be obtained without executing an iteration process, and the reservoir oil-water relationship can be further analyzed according to the inversion result.

The application discloses a kind of group level fMRI brain function network analysis methods based on graph convolutional neural network, comprising: obtaining the fMRI of the brain of multiple groups of different categories of subjects, after preprocessing, establish brain function network for each subject, carry out single sample t test to each group brain function network, calculate the graph theory attribute of node as node feature;Establish and train the classification model of GCN, the input of neural network is the edge of brain function network, node feature, and the output is the category of subject;According to the explainability of GCN, find the most important subgraph structure for classification, the subgraph represents the biggest brain function connection of the difference of brain function network between different groups, and can be used for further analysis on the neural mechanism of brain disease.The application can more comprehensively compare the brain function network of different groups, obtain more accurate results, and can be widely used in aphasia, depression, alzheimer's disease and other brain function network analysis of brain disease.

A method of analyzing an interference peak of a sample spectrum generated using a detector of an optical spectrometer is provided. The interference peak is produced by a plurality of spectral emissions of different wavelengths. The method includes generating, based on a model of expected curve parameters of the optical spectrometer and a location of the sample peak on the detector of the optical spectrometer, interference curve parameters representing a shape of each spectral emission in the interference peak. The method further includes fitting a plurality of curves to the interference peak, each curve corresponding to one of the plurality of spectral emissions of different wavelengths that form the interference peak, wherein each curve is fitted using the interference curve parameters provided by the model of expected peak parameters. The plurality of curves are output for further analysis.

The application discloses a motion electrocardio processing method and system, and relates to the technical field of electrocardio signal processing. The motion electrocardio processing method provided by the application comprises the following steps: acquiring electrocardio signals under different motion states; performing empirical mode decomposition on the motion electrocardio to decompose the electrocardio signals into a plurality of intrinsic mode functions, and performing high-pass filtering operation on the low-frequency intrinsic mode functions to suppress low-frequency noise in the motion electrocardio; then, performing variational mode decomposition on the remaining components to more accurately extract variational mode functions of different frequencies, and performing center frequency judgment on each variational mode function; for the mode functions smaller than the center frequency, non-local mean noise reduction is adopted; for the mode functions greater than or equal to the center frequency, singular value decomposition is adopted to reduce noise and realize baseline interference suppression; finally, the mode functions after noise reduction are reconstructed, the wavelet threshold method is adopted to reduce noise for the electromyographic interference noise in the motion electrocardio, so that a clean motion electrocardio signal waveform after noise interference suppression is obtained, and subsequent further analysis is facilitated.

The present application relates to the technical field of computer-aided design, in particular to a kind of well lane blasting area's polyline hole arrangement optimization method and system.The present application first filters out the historical reference project of current blasting project, to determine the basic hole arrangement parameter of current blasting project based on the hole arrangement design parameter of each historical reference project;Further analyze the engineering basic information deviation of current blasting project and historical reference project, and the drilling positioning error deviation of the drilling equipment of current blasting project and historical reference project, to obtain hole arrangement correction coefficient;Finally, the basic hole arrangement parameter is adaptively corrected using hole arrangement correction coefficient, to determine the polyline hole arrangement parameter of current blasting project.The present application quantifies rock mass characteristics and equipment deviation by historical similar blasting project matching and blasting condition deviation comparison to adaptively adjust hole arrangement parameter, optimizes the polyline hole arrangement of well lane blasting area, and improves the economy and safety of blasting design.

The invention discloses an industrial internet security operation and maintenance risk assessment method and system, and relates to the technical field of data management.The industrial internet security operation and maintenance risk assessment method comprises the steps that initial information collection is conducted on an industrial internet operation environment, risk links are constructed, and primary risk links are further analyzed and screened out; then obtaining dynamic operation data of each primary risk link, performing priority ranking based on the dynamic comprehensive risk score, and dynamically adjusting response triggering measures of the primary risk links at the same time to realize pertinence and timeliness of risk disposal; and finally, through cross validation of multi-level risk links, evaluation of actual effects of each adjustment measure and fine adjustment of a network security policy, potential risks can be effectively identified and quantified, a protection policy can be dynamically adjusted, the high-risk link disposal efficiency can be improved, and the security and stability of the whole system can be ensured. Therefore, the scientificity, controllability and reliability of industrial internet security operation and maintenance are obviously enhanced.

The present application relates to the technical field of microwave sensor, in particular to a wearable multi-modal flexible microwave sensor.The present application obtains a deformation state value by analyzing deformation data, analyzes stress data to obtain a stress state value, further analyzes element connection state information, then performs corresponding adaptive adjustment according to the element connection state, obtains an adaptive adjustment result, can monitor and analyze the connection of each internal element to obtain the corresponding connection state, can facilitate the user to find abnormalities in time and increase controllability; also, the signal state is obtained by identifying the signal comprehensive parameter, the corresponding signal gain control instruction is generated, the signal gain is executed according to the signal gain control instruction, the corresponding signal abnormal information is generated according to the gain execution, the result of the signal gain is judged, the connection state is confirmed again, and the accuracy of monitoring element connection abnormality is increased.

The invention provides a wavelet analysis method and device for a traveling wave head, electronic equipment and a storage medium, and relates to the technical field of power systems. The wavelet analysis method of the traveling wave head comprises the following steps: at a wave recording triggering moment, carrying out wavelet decomposition on an obtained fault complete waveform to determine a modulus maximum array of the fault complete waveform; determining a final screening threshold coefficient according to a preset initial threshold coefficient, a preset threshold screening mode and the modulus maximum array; and according to the final screening threshold coefficient, a preset wave head position determination mode and the modulus maximum array, determining the position of a traveling wave head point in the complete waveform of the fault. According to the method and the device, the final screening threshold coefficient is dynamically adjusted and determined according to the preset initial threshold coefficient and the threshold screening mode in combination with the specific characteristics of the modulus maximum array, and adaptive adjustment of the threshold parameter is realized. And further analyzing the modulus maximum array, and accurately finding out a modulus maximum point meeting the condition as the position of the traveling wave head.

Arrangements for cyber threat detection are provided. In some aspects, a request for an enterprise event may be received. The request may be received from a user computing device and may include a user agent string associated with the user computing device. The user agent string may be parsed to identify parameters associated with the request. The request may then be analyzed to determine whether it is unauthorized. Analyzing the request may include comparing the identified parameters to known viable parameters identified from historical hardware and / or software data. Based on the comparing, if the identified parameters match the known viable parameters, the requested event may be processed. If the identified parameters do not match the known viable parameters, the event may be flagged and transmitted to an enterprise organization computing system for further analysis and / or executing of one or more mitigation actions.

The invention discloses a Fresnel lens calibration method and a Fresnel lens, particularly relates to the technical field of industrial visual inspection, and is used for solving the problem that an existing focal length calibration method based on machine vision is easily interfered by inherent annular textures of the Fresnel lens, so that the measurement accuracy and reliability are insufficient. A focusing light spot image sequence is collected by moving an image sensor along an optical axis, then a radial intensity distribution gradient feature evaluation value and an annular texture edge gradient saliency evaluation value of each image are calculated, and a candidate focus area is identified by analyzing the correlation of the two evaluation value sequences; then, the convergence of the position change trajectory of the preset feature points is analyzed in the candidate region to determine an optimized candidate region, the symmetry of the evolution trajectory of the morphological descriptor is further analyzed to accurately position and optimize the focus position, and finally the focal length is calculated; the interference of annular textures can be effectively suppressed, and the precision and robustness of focal length verification of an industrial vision system on an automatic production line are improved.

The application provides a kind of hierarchical defrosting heat pump intelligent control method and system, method includes: collecting fin import and export temperature calculation temperature difference and its change rate, assesses the initial state of frost formation;Then combine water side heat exchange efficiency and its attenuation rate, further analyze the influence of frost layer on system performance.Based on this information, the controller uses the frost formation level score function to accurately determine the mild, moderate and severe frost formation state, and automatically matches the corresponding defrosting strategy: maintain heating mode and online defrosting when mild;Progressive bypass defrosting is used when moderate;Switch to refrigeration mode and dynamically adjust the expansion valve opening when severe to enhance the defrosting effect.The application not only improves the defrosting accuracy and system stability, but also improves the operating efficiency and user experience of heat pump equipment in low temperature environment.

Techniques for compiling switch blocks are disclosed. One or more embodiments analyze and rewrite a set of pattern labels in a switch block in a compile-time process for determining whether the switch block is exhaustive. At compile-time, a system populates a set with case labels from a switch block. The system applies a set of rules to iteratively re-write pattern labels in the set and checks whether the re-written set, and hence the original switch block, is exhaustive. If the compiler determines that (a) the set does not appear to be exhaustive, and (b) the set includes patterns labels, then the compiler determines whether the set may be rewritten before further analysis. The compiler iteratively re-writes and re-analyzes the case label set for exhaustivity until the case label set is determined to be exhaustive or cannot be rewritten further.

The application relates to the technical field of energy management, and particularly discloses an integrated energy management system of a light storage building based on edge computing, which comprises a data acquisition module, which is used for collecting inverter operation state data, current sensor operation state data and light data in the energy management system in real time through edge computing. The operation state of the inverter can be analyzed by combining the data collected in the data acquisition module, and whether the operation state of the inverter is abnormal can be analyzed. Then, the performance of the inverter can be evaluated in real time by combining the real-time collected energy storage system data, and whether the performance change of the inverter will cause additional energy loss and reduce the power generation efficiency can be further analyzed. Then, whether the inverter optimization operation is needed is decided based on the analysis result, that is, the inverter can be optimized to avoid the occurrence of the condition of reducing energy loss, so that the power generation efficiency is improved and the energy utilization is maximized.

The invention relates to the technical field of power electronics and transformers, in particular to a solid-state transformer fault early warning and isolation system based on protocol access, which is characterized in that when a solid-state transformer is started, a data frame generated by equipment is collected and stored by a sensor through a local control module; the protocol analysis module receives the equipment data frames and extracts information in the equipment data frames in a dynamic analysis mode; the fault early warning module analyzes the analyzed data based on a fault prediction model, predicts possible transformer faults, and evaluates prediction results; the fault isolation module is used for positioning equipment needing to be isolated according to an evaluation result, and generating related fault information by disconnecting the equipment from other components; the communication interface module reports the fault information to a central server or superior equipment for further analysis and processing of fault data; according to the system, real-time monitoring, fault early warning and intelligent isolation of the solid-state transformer are realized, and the operation safety and stability of equipment are improved.

The application relates to the technical field of deep learning, in particular to an arthroscopic image real-time abnormality recognition system based on deep learning. The system identifies the high light performance degree of different regions in the image during model recognition, analyzes the high light performance and the probability value deviation of interframe pixel points, extracts the dynamic change trend of the target region pixel points in continuous frames, and obtains the consistent degree of performance in time sequence. The system further analyzes the covering influence of high light on the region in the time sequence video, and then adjusts the segmentation confidence degree by using the segmentation degree of the pixel points in the initial target region which is influenced by the high light, compensates the region information which is partially blocked or blurred through multi-frame fusion, and obtains the final target region for model training. Through interframe high light feature and region fusion processing, the application reduces error accumulation and high light interference, obtains a more accurate and complete abnormal region, and effectively improves the recognition precision of deep learning.

This invention relates to the field of phase-locked loop (PLL) technology, specifically to a method for achieving low jitter over a wide locking range by optimizing the PLL structure. The method includes: outputting a first error signal through a phase detector, obtaining the characteristics of the first error signal, and constructing a first error signal function; obtaining a first adaptive loop bandwidth; constructing a multi-band voltage-controlled oscillator (VCO) matching mechanism, including VCOs in multiple frequency bands; during PLL locking, selecting a frequency band based on the input signal frequency and the center frequency band of the VCO to obtain a first matching frequency band; and calculating the root mean square (RMS) of the first error signal based on further analysis of the first error signal characteristics, and combining the initial feedback gain and the initial error signal RMS to obtain a first feedback gain adjustment value. This invention achieves low jitter over a wide locking range through adaptive loop bandwidth adjustment, a multi-band VCO matching mechanism, and adaptive feedback gain adjustment.

A method and system for automatically detecting within an input 3D ultrasound image one or more suspected disease features and automatically computing an optimum one or more planes through the 3D field of view for acquiring imagery that would best assist in confirming or further analyzing the suspected disease features. The determined optimum one or more planes are used to control acquisition by an ultrasound acquisition system of new 2D images which correspond to said determined optimum planes.

The application discloses a kind of multi-user physical layer security transmission methods based on time modulation array, belong to physical layer security technical field, construct space division multiple access security communication system based on time modulation array, including base station end and receiving end, base station end is equipped with time modulation array by N antenna, receiving end includes legal user and eavesdropper, and legal user is equipped with single antenna;In the case of single antenna eavesdropper, I group data streams are transmitted to I single antenna legal users in parallel by space division multiple access technology;Base station pre-encodes data signal, then the data signal after pre-encoding is mapped to OFDM signal, then time modulation technology is used to transmit OFDM signal to legal user.The application is particularly related to the realization of multi-user physical layer security communication using single radio frequency chain time modulation array, and further analyzes the lower bound of system security rate based on time modulation considering the harmonic characteristics of time modulation.

The present application relates to the technical field of data processing, and more particularly to a food processing equipment intelligent supervision method based on data analysis, which, in the food processing process, when detecting that the equipment in the food processing process appears suspected abnormality, obtains a early warning time length basic value in combination with the recent equipment comprehensive temperature change characteristics and the processing time length; further analyzes from two core dimensions of the processing process stability and the food material itself thermodynamic characteristics, respectively calculates to obtain a process instability adjustment factor and a thermodynamic characteristic adjustment factor, optimizes and calibrates the early warning time length basic value by using the two adjustment factors, finally obtains an adaptive early warning time length, and performs abnormal supervision on the equipment, so that the early warning time length is more suitable for the actual production scene, so as to accurately adapt to the complex and changeable processing conditions, effectively improve the precision of the equipment early warning monitoring in the food processing process, and further ensure the food processing quality stability.

The application discloses a city air pollution monitoring management system and method based on an LSTM model, relates to the technical field of air pollution monitoring, and discloses an air pollution monitoring module, an air pollution monitoring point analysis module, a city air pollution comprehensive module, and an air pollution type classification module. The air pollution monitoring module and the air pollution monitoring point analysis module are arranged, the collected data of various air pollution monitoring points in the city are analyzed in depth, the subsequent air pollution situation is accurately predicted by using an LSTM model, the optical reaction effect of various types of city air pollution data is predicted, the pollution fluctuation of the air pollution monitoring points is combined, the air pollution situation of the air pollution monitoring points is comprehensively judged, the air pollution monitoring points with significant pollution are marked, the city air pollution comprehensive module and the air pollution type classification module are arranged, the correlation of pollution regions between the significant pollution points is further analyzed, and then the air pollution situation of the city is comprehensively analyzed.

The utility model belongs to the technical field of shooting training equipment, and discloses an intelligent shooting auxiliary glove with a sensor array, which comprises a glove and the sensor array, the glove palm side comprises a finger face area and a palm face area, and the finger face area is formed by combining four finger faces except the thumb. The sensor array comprises a finger sensor assembly arranged in the finger face area and a palm sensor assembly arranged in the palm face area. The finger surface area comprises a finger pulp area and a finger joint area, and the finger sensor assembly comprises a finger pulp sensor arranged in the finger pulp area and a finger joint sensor arranged in the finger joint area. According to the utility model, the mechanical distribution characteristics of the palm during shooting are analyzed, and the plurality of pressure sensors are reasonably arranged in the finger surface area and the palm surface area of the glove, so that the pressure values of each finger surface position and each palm surface position are flexibly detected, and the shooting posture can be further analyzed conveniently based on the pressure values so as to achieve the posture training and correcting effect.

The application provides a method for detecting HES1 markers of melanoma CTCs by using multiplex immunofluorescence technology, and the method comprises the following steps: S1, collecting peripheral blood of a melanoma patient, and performing sorting and enrichment of CTCs through a microfluidic chip; S2, fixing and permeating after making all nucleated cells in the blood flatly and enriched on an adherent slide through a centrifuge; and sequentially performing co-incubation of HES1, HMB45 and CD45 antibodies on the screened melanoma CTCs through the multiplex immunofluorescence technology, and further analyzing the expression of the HES1 antibody and performing image analysis and quantification. The method is simple in operation, high in detection accuracy, can avoid false negatives and the like, and improves the screening accuracy of melanoma CTCs.

Systems and methods for tracking and logging mute events that occur during communication sessions are disclosed. The method includes generating a disruption report that includes one or more aspects of a mute event detected during a communication session. The disruption report is communicated to a network for further analysis.