88 results about "Statistical processing" patented technology

The present invention obtains a rider-assistance system, a data structure, and a control method capable of improving safety of a straddle-type vehicle.In a rider-assistance system 1, the data structure, and the control method according to the present invention, an execution section of the rider-assistance system 1 executes rider-assistance operation to assist with driving by a rider. Furthermore, a generation section of the rider-assistance system 1 generates travel guide information on a curved road by subjecting a data set, which includes turning travel information of each of plural straddle-type vehicles 10 on the curved road acquired by each of the vehicles, to statistical processing. Before the straddle-type vehicle 10 enters the curved road, the execution section executes the rider-assistance operation on the basis of the travel guide information that is generated by the generation section.

The invention relates to the technical field of chips, and provides a clock duty ratio calibration method and device, a medium and a program product, and the method comprises the steps: obtaining a plurality of intermediate control values used for representing a duty ratio state through executing a scanning process for multiple times; the at least one intermediate control value is a control value recorded when the duty ratio detection signal is subjected to final state transition in one scanning process; performing statistical processing on the plurality of intermediate control values to generate a final duty ratio control signal; the clock duty cycle is calibrated based on the final duty cycle control signal. According to the invention, the problems of poor anti-interference capability, misjudgment caused by the influence of a circuit environment and low single comparison precision in the prior art are solved, the clock duty ratio distortion caused by the mismatch of a duty ratio detection circuit and environmental noise is avoided, and the bit error rate is effectively reduced.

The invention relates to the technical field of life cycle management, in particular to a spinning machine life cycle management system based on a big data model, which comprises a signal analysis module, a pass division module, a state recognition module, a cycle modeling module and a cycle optimization module. According to the method, spinning pressure signals and time sequences are synchronously collected and subjected to peak-valley scanning, pressure fluctuation data are formed, details of the machining process are continuously depicted, pass boundary judgment and section statistical processing are combined, pressure change rules are structurally presented, continuous fluctuation section combination is promoted through consistency verification, and the machining precision is improved. A clear mark is obtained for an implicit operation state, a state transition relation is deduced based on cross-pass occurrence frequency and continuous characteristics, an equipment behavior evolution path tends to be clear, a life cycle result is converted from static recording to dynamic analysis, and after deviation distribution verification and grading adjustment are synchronously introduced, a pressure abnormity trend can be identified in time. The maintenance rhythm and the real working condition are kept coordinated, and the management judgment basis is more coherent.

An event data recording section 240 records event data related to an activity executed by a plurality of players. A play time acquisition section 212 acquires play times of the plurality of players for a task from event data indicating start of the task included in the activity and event data indicating end of the task included in the activity, the event data being recorded in the event data recording section 240. A statistical processing section 220 sorts each of the players into any one of a plurality of classes according to a skill value of the player and determines, on a basis of the play times of the plurality of players in each class, a representative value for the play times in each class for the task. The statistical processing section 220 aggregates representative values for each class to determine the representative value for the play times in each class for the activity.

We provide the system. [Solution] A means of receiving voice instructions from a user and converting them into text using speech recognition technology, A means for generating an information gathering task based on the converted text and distributing it to an information sharing device, A means of collecting input from an information sharing device and analyzing the data through statistical processing, Means for providing analysis results to users, An information processing system that includes this.

Provided are an information processing device, an information processing method, a program, and an information processing system, by which formulation of a preferable survey plan for each analysis area in a survey target region is realized. The information processing device acquires map information in which a geographical position and a shape are represented, acquires feature meta-information pertaining to each of one or more features, acquires an aerial image of a survey target region, derives a degree of damage for each of the one or more features on the basis of the map information, the feature meta-information, and the aerial image, statistically processes the degree of damage derived for each of the one or more features included in an analysis area, derives representative information representing the analysis area, and causes the analysis area to be displayed in association with the representative information.

The invention provides a method, a device and a system for rapidly analyzing components of direct coal liquefaction oil based on a near infrared spectrogram, the method comprises the following steps: acquiring sample data of the direct coal liquefaction oil, the sample data of the direct coal liquefaction oil comprises near infrared spectrum data of the direct coal liquefaction oil and detailed composition data of sample oil; according to sample data of the direct coal liquefaction oil and a ResNet-34 neural network algorithm, the near infrared spectrum of the liquefied oil with known composition is subjected to statistical treatment, the relationship between the liquefied oil composition and a spectrogram is obtained, then the near infrared spectrum spectrogram of an unknown oil product is identified to predict the composition, and a direct coal liquefaction oil component prediction model is established. The prediction model is at least used for component prediction of the direct coal liquefaction oil; the direct coal liquefaction oil in the production process is analyzed according to the prediction model obtained through training, online analysis can be rapidly and accurately conducted on the direct coal liquefaction oil, and the method can solve the technical problems that in the direct coal liquefaction oil production process, analysis is difficult, and the analysis period is long relative to the production period.

The invention discloses a clustering analysis-combined statistical multi-dimensional data processing system and method, and aims to solve the problems of low quality, poor precision, insufficient value mining and the like of a traditional data processing technology in a multi-dimensional data scene. The system comprises a data acquisition module, a preprocessing module, a clustering analysis module, a statistical processing module, a result output module and a storage module. The method sequentially executes the steps of data acquisition, preprocessing, clustering analysis, statistical processing, result output and storage. In the acquisition stage, data are integrated through a multi-source interface and formats are unified, in the preprocessing stage, abnormal values are removed through the 3 sigma principle, missing values are supplemented through multiple methods, in the clustering stage, the optimal clustering number is determined by depending on a contour coefficient or a Clinski-Harabasz index, in the statistical stage, deep association is mined for all clusters, in the output stage, diversified forms are provided, and in the storage stage, data safety is guaranteed. According to the scheme, data quality and analysis precision are improved, and reliable support is provided for data-driven decision-making of each industry.

The application discloses a severe ground spectrum compiling method based on severe fleet damage, which comprises the following steps: inputting the use requirements of an airplane, and outputting the composition and proportion of a mission profile; pre-processing measured load data, wherein the pre-processing comprises filtering and overload standardization; statistically processing the load data to obtain a measured ground overload cumulative overrun number curve family of a taxiing mission section and an overload cyclic peak-valley value of a landing impact mission section; statistically analyzing the damage of a load sequence of each mission profile, taking the damage of each mission profile whose reliability reaches a first preset value as severe damage of each mission profile, and inversely calculating severe ground overload cumulative overrun number curves of the taxiing mission section and each level load of the landing impact mission section corresponding to the severe damage; and compiling a severe ground overload spectrum. The severe spectrum compiled by the method can significantly reduce the test time and can fully expose the use cracks.

The embodiment of the invention relates to the technical field of instrument data control, and discloses a non-invasive electrode closed-loop feedback stellate ganglion block electrical stimulation device. According to the device, a control module carries out closed-loop control processing on initial stimulation parameters and carries out adjustment processing on stimulation frequency and intensity to obtain an optimal control parameter combination; the acquisition module performs multi-dimensional acquisition processing on the physiological parameters to obtain a monitoring data set and performs threshold judgment processing to obtain a stimulation control instruction; the analysis module performs dynamic analysis processing on the monitoring data in the stimulation process to obtain a parameter adjustment strategy, and performs optimization processing on the stimulation parameters according to the parameter adjustment strategy to obtain steady-state control parameters; and the evaluation module carries out integration analysis processing on the control process data to obtain a working state evaluation index, and carries out classified statistical processing according to the evaluation index to obtain a parameter optimization suggestion. According to the invention, adaptive adjustment of the stimulation parameters and intelligent evaluation of the system operation state are realized.

This invention discloses a real-time intelligent monitoring processing method and system based on edge computing, comprising the following steps: acquiring edge monitoring data and processing it to obtain an identifiable monitoring video stream; performing segmentation and standardization processing to obtain a standardized candidate video segment set; performing lightweight visual feature extraction to obtain a segment-level visual feature sequence; generating edge inference gating parameters based on the edge monitoring data; obtaining a segment anomaly score sequence based on an edge-gated lightweight anomaly segment localization network; generating a scene adaptive judgment threshold and correcting it to obtain a corrected segment anomaly score sequence; determining the anomaly segment localization result and statistically processing it to obtain anomaly segment statistical features and generate a segment risk level; extracting anomaly video segments from the standardized candidate video segment set and obtaining the intelligent monitoring real-time processing result based on the segment risk level. This invention utilizes edge inference gating to achieve anomaly segment localization, possessing the advantages of low latency, low bandwidth, and high stability.

The application relates to the technical field of region division, and discloses a region division method, system, terminal and storage medium based on edge segmentation and clustering analysis, the method comprising the following steps: obtaining a target building of a target region, performing statistical processing on the grid flow of the target building, performing edge segmentation, and obtaining an initial division result; constructing a boundary buffer zone, performing gradient calculation to obtain gradient data, and performing position correction on boundary nodes of an initial spatial boundary to obtain a division optimization result; performing region clustering processing on the division optimization result according to clustering parameters to obtain a clustering result, and performing screening processing on the clustering result to obtain a target clustering result; and performing smoothing optimization on the boundary of all clustering regions of all levels in the target clustering result to obtain a target division result. Through integrated processing of initial grading, edge correction, density clustering and geometric smoothing, the stability of data statistics and the accuracy of the division result are improved.

The application provides a high duty cycle signal discrimination and suppression method for a wide bandwidth space receiver, comprising the following steps: step 1, background noise statistical processing; step 2, amplitude calculation and peak searching processing; step 3, peak signal space resolution; step 4, single beat statistical quantity generation; step 5, multi-beat statistical quantity generation; step 6, duty cycle calculation and cross-frequency point merging; step 7, high duty cycle signal judgment; and step 8, high duty cycle signal suppression threshold generation and output. The high duty cycle signal discrimination result and the high duty cycle signal suppression threshold generated by the application play a role before the generation of the pulse level detection result, are a beneficial supplement to the traditional background noise statistical threshold and the CFAR threshold, can filter out the high duty cycle signal in the pulse detection stage, can greatly reduce the processing pressure of the subsequent pulse pretreatment and signal sorting, and can improve the interception quality of the receiver to the normal radar pulse signal.

This information processing device comprises: a classification unit that clusters customers on the basis of customer data that is attribute information of the customers, which are supplied with power; a temperature data calculation unit that performs weighted averaging, in one cluster, of temperature data indicating the temperatures of respective weather areas, which are a specific geographical ranges in which similar weather forecasts are provided, at a reference time in accordance with the number of customers belonging to the respective weather areas and thereby calculates, on a per-cluster basis, cluster temperature data that is the temperature data at the reference time corresponding to the one cluster; and a model learning unit that generates, by machine learning on a per-cluster basis, a power demand prediction model for predicting the power demand of one customer in a cluster, said power demand prediction model setting at least the cluster temperature data as a feature and setting a statistical value obtained by statistically processing the power consumption of the customers included in the cluster at the reference time as a target.

The application provides a weak power grid resonance detection method and device. Distortion detection is performed on a grid-connected signal obtained through sampling. A plurality of distortion detection results in a detection time window are statistically processed to determine the waveform distortion degree of the grid-connected signal in the detection time window. Whether the distortion is continuously aggravated is determined based on the statistical value of the distortion results in the detection time window, and whether weak power grid resonance occurs is determined. The weak power grid resonance is accurately detected without affecting the quality of the grid-connected current, and the method is simple and reliable.

A kind of composite clock establishment method based on atomic clock and millisecond pulsar, reads millisecond pulsar timing observation data, calculates the timing residual of millisecond pulsar;The timing residual of millisecond pulsar is sorted according to the order of reduced julian day;Millisecond pulsar that meets the establishment of millisecond pulsar time is screened out;Whether the timing residual of screened millisecond pulsar is multi-band timing residual is judged;Multi-band timing residual is statistically processed to obtain average timing residual;Average timing residual and timing red noise identification and processing of timing residual that is not multi-band are carried out, to obtain millisecond pulsar time time sequence;Read atomic clock measurement comparison data, carry out frequency and phase jump identification and rough error detection preprocessing, to obtain atomic time time sequence;Millisecond pulsar time and atomic clock time are combined using Vondrak-Cepek combination method to obtain composite clock Q.The composite clock established by the present application has both millisecond pulsar time long-term stability and accuracy, and also has atomic clock time high resolution and medium and short-term stability.

The application discloses a voltage flicker acceleration detection method, a system and a medium, and relates to the technical field of voltage flicker detection.The voltage flicker acceleration detection method comprises the following steps: squaring a voltage signal to be detected to obtain a voltage square signal; calculating the direct current bias of the voltage square signal; subtracting the direct current bias of the voltage square signal from the voltage square signal to obtain an optimized voltage signal; and performing demodulation filtering, visual sensitivity weighting filtering, squaring, smoothing weighting filtering and statistical processing on the optimized voltage signal to obtain a voltage flicker detection result.The application aims to solve the problem of slow convergence speed of the demodulation filtering of the current IEC flicker detection method, which affects the accuracy of flicker detection, and improve the accuracy of detection while ensuring the speed of flicker detection.

To realize an advertising effectiveness display system and advertising effectiveness output device that contribute to improving the effectiveness of advertising prints. [Solution] In processing C20, the advertising server 100 generates advertising effectiveness notification data. The advertising effectiveness notification data is generated by performing statistical processing based on advertising information stored in the advertising management DB 131a associated with identification information 139, the history of actions (date and time of actions) stored in the action history DB 133 associated with both user identification information and identification information 139, and user information stored in the user information DB 134 associated with user identification information. At this time, necessary information is statistically extracted based on the advertising effectiveness display format information stored in the advertising management DB 131a associated with identification information 139, and advertising effectiveness notification data is generated.

This invention relates to an adaptive correction method for multi-beam backscattering based on statistical change point detection, belonging to the field of marine acoustic detection and seabed sediment information processing technology. The method includes acquiring multi-beam data and constructing a proxy signal; adaptively grouping the proxy signal sequence using a bisection change point detection algorithm; statistically processing the backscattering intensity data within each group according to the beam incident angle; smoothing the average angle response curve using a smoothing filter; unsupervised partitioning based on curvature and clustering; and constructing a piecewise angle response reference model, using this model to calculate the correction value. This invention effectively weakens the central highlighting, edge attenuation, and striped structure caused by angle response effects, reduces the block effect or mosaic phenomenon common in fixed window methods, and effectively weakens the systematic correlation between backscattering intensity and incident angle, providing a more stable and reliable data foundation for subsequent seabed sediment classification, seabed geomorphological identification, and related marine survey applications.

The invention provides an intelligent multi-stream write-in method and device based on interval write frequency distribution and a server, and relates to the technical field of solid state disks, and the method comprises the following steps: carrying out segmentation processing and statistical processing on an SSD, and determining normal distribution of large particle interval write frequencies of the SSD; analyzing, calculating and processing the normal distribution through a flow model of a preset plurality of flows, counting writing flows of different addresses to obtain an average value and a variance of writing times of a large-particle interval, and constructing standard normal distribution according to the average value and the variance; performing threshold calculation according to the probability density function of the standard normal distribution and a cold and hot flow division proportion of a flow model of a preset plurality of flows, and determining a target write frequency threshold of cold data and hot data division, so as to perform cold and hot flow division on the data of the SSD by using the target write frequency threshold, and respectively writing the cold data and the hot data into the corresponding flash memory areas during data writing and data recovery. According to the method, the write amplification of the SSD can be remarkably reduced, so that the service life of the SSD is prolonged.

The present invention provides a radiation analysis method, a radiation analysis device, and a computer program which are for specifying the amount of an element contained in a sample on the basis of the detected intensity of radiation.　Provided is a radiation analysis method wherein a radiation detector is used to detect radiation produced from a sample, response waves that are included in an output signal outputted from the radiation detector in response to radiation detection are counted to specify a detected intensity of radiation, sum-peak correction is performed to correct, via statistical processing, the detected intensity of radiation which is affected by the overlapping of a plurality of response waves, and the amount of a specific element contained in the sample is calculated on the basis of the detected intensity of radiation that has been corrected by the sum-peak correction.

This computer system is communicably connected to a machine learning model, receives an evaluation instruction including information designating a use case of the machine learning model, an input prompt, and an expected answer, generates a plurality of conversion prompts by changing an expression pattern of the input prompt, inputs the input prompt and each of the plurality of conversion prompts to the machine learning model to acquire a plurality of generated answers, compares the expected answer and the generated answer for each of the plurality of generated answers, calculates a similarity score indicating a similarity between the expected answer and the generated answer, and executes statistical processing using the similarity score of the plurality of generated answers, to thereby calculate an index for evaluating the answer accuracy of the machine learning model in the use case.

The invention provides a data processing method and device, electronic equipment and a storage medium, and the data processing method comprises the steps: sending a query instruction to equipment to be monitored, so as to enable the equipment to be monitored to return first target data to a monitoring platform according to the query instruction. And according to the first target data, locking a calibration date with data exception, and according to the calibration date, executing a data calibration process to realize targeted repair of the corresponding data. The data calibration process comprises the following steps: acquiring second target data of a date corresponding to a calibration date in a monitoring platform and first historical data of the monitoring platform; and performing statistical processing on data of a date corresponding to the calibration date in the first target data to obtain third target data, and updating the second target data into the third target data. And when the second target data is updated to the third target data, performing data synchronization processing on the first historical data of the monitoring platform to obtain the second historical data, so that the integrity, accuracy and consistency of the data of the monitoring platform are improved.

A processing system distributes original data in a state of being segmented shares to a plurality of servers, and stores the original data in the servers, each of the servers including processing circuitry configured to perform statistical processing on the shares that are used in computation that is requested by a requesting device, and perform the computation that is requested by the requesting device using statistical information into which conversion is made in the statistical processing.

The application provides an event statistical processing method, a terminal device and a storage medium. The event statistical processing method comprises the following steps: in response to receiving periodic signal data, performing state verification on the periodic signal data; if the verification result meets a first preset condition, performing first preset processing on the periodic signal data; otherwise, performing second preset processing on the periodic signal data. The event statistical processing method, the terminal device and the storage medium of the application can continuously obtain each piece of periodic signal data in real time through an online processing mechanism, and different processing is performed according to different states of data feedback according to the state verification result. The real-time performance of the periodic signal data is guaranteed, the read-write speed is accelerated, the single operation time is greatly reduced, and the calculation efficiency is improved.

The invention discloses a garbage detection method and system based on multi-modal information fusion, and relates to the technical automatic driving and robot field, and the method comprises the steps: collecting an RGB image and a depth image in real time through a depth camera; detecting the RGB image, and outputting a garbage category label and two-dimensional position information; determining a corresponding depth sampling area on the depth image; extracting depth values of all pixels in the depth sampling area to form a depth value set; robust statistical processing is carried out on the depth value set, and a depth value representing a corresponding garbage body distance is calculated; calculating a confidence score based on the depth value set; calculating three-dimensional space coordinates of the corresponding garbage; integrating the category label, the three-dimensional space coordinate and the confidence score of each garbage into a structured data record; and forming all the junk data records into a target list and outputting the target list. According to the invention, high-resolution RGB identification is combined with low-resolution depth positioning, so that the method is visible and accurate.

The invention relates to a training method of an image super-resolution model, an image super-resolution method and device, electronic equipment, a storage medium and a program product, and the training method comprises the steps: carrying out the degradation processing of at least two different degradation modes on a sample image, and obtaining at least two degraded sample images; processing the at least two degraded sample images by using an image super-resolution model to obtain sample intermediate features of the at least two degraded sample images; performing statistical processing on the sample intermediate features of the at least two degraded sample images to obtain at least two groups of feature statistics, the at least two groups of feature statistics being in one-to-one correspondence with the at least two degraded sample images, and each group of feature statistics comprising at least one feature statistics; determining feature loss according to the at least two groups of feature statistics; and adjusting parameters of the image super-resolution model based on the feature loss so as to train the image super-resolution model. The training method can give consideration to the detail recovery ability and generalization ability of the model.

The invention provides a laser ranging device, and belongs to the technical field of laser detection and ranging. The system comprises a single photon detection module integrated on a single chip and a processing module electrically connected with the single photon detection module. And the single-photon detection module is integrated with a single-photon detector array, a time measurement unit array and a histogram processing unit, and is used for accumulating and generating a flight time histogram in the chip according to the flight time of returned photons detected for multiple times. The processing module is configured to obtain the time-of-flight histogram from the single photon detection module, perform peak detection on the histogram to determine a signal peak corresponding to the target distance, and calculate the target distance based on the position of the signal peak. By means of on-chip integration and statistical processing, miniaturization and low power consumption of the system are achieved, and the precision and reliability of weak signal distance measurement under the strong noise background are improved.

This disclosure relates to the field of data processing technology, specifically disclosing an operational data processing method, apparatus, computer equipment, storage medium, and computer program product. It acquires a scheduled task for processing operational data, and obtains a scheduled task matching the current time based on the task's start time as a task to be executed. Then, based on the task type of at least one subtask of the task to be executed, it executes each subtask of the task to obtain the target data after task execution. Since the scheduled task can perform marking and statistical processing on the operational data, it eliminates the need to add object marking code at key transformation points, thus decoupling the marking and statistical code and accelerating response speed. Furthermore, since the scheduled task can flexibly configure its execution time, it can be configured to execute during idle times, not only avoiding the risk of task failure but also further improving response speed, thereby increasing data processing efficiency.

The invention discloses a model training hotspot identification method combining cross-thread dependence and multi-critical path analysis, and the method comprises the steps: 1, collecting an event execution track in a model training process, and carrying out the analysis and standardization of collected original performance data into a unified format; 2, aggregating the original events through hierarchical modeling, and constructing edge connection based on a dependency relationship to form a calculation dependency graph; step 3, identifying a decisive event sequence and a critical path in a model training process based on the constructed calculation dependency graph; and 4, through overlap analysis and statistical processing, extracting hot events with actual optimization values from the key paths, and generating a sorting list. The invention further discloses a system for implementing the method, and the system has a wide application prospect.