168 results about "Statistical algorithm" patented technology

The invention relates to a blueberry sugar degree hyperspectral inversion method based on improved Laplacian mapping, and belongs to the technical field of blueberry hyperspectral acquisition and analysis. The method comprises the following steps: firstly, collecting blueberry samples of different varieties and different maturity degrees in a complex scene, measuring hyperspectral data of the blueberry samples and sugar content of corresponding blueberries, then dividing the blueberry sample data into a training set and a test set according to a proportion by using a statistical algorithm, and performing data preprocessing by using an MSC + FOD combination to obtain a training set and a test set; extracting characteristic wave bands from the preprocessed spectral data by using ILE provided by the invention, establishing a blueberry sugar degree prediction model by using CNN, training the model by using a training set, and finally comprehensively evaluating the model by using a test set in combination with regression indexes R2, RMSE and RPD. The method can quickly and accurately predict the sugar degrees of blueberries of different varieties and different maturity degrees in a complex scene, and provides powerful technical support for prediction of the sugar degrees of the blueberries.

The invention relates to the technical field of computer vision, in particular to an improved 4S store passenger flow statistical algorithm based on pedestrian re-identification, which comprises the following steps of acquiring video data of a 4S store entrance in real time based on image acquisition equipment, performing standardization processing on the video data in combination with an input requirement of video processing, and generating a video standardization processing result. According to the method, the image quality and the data processing continuity under the condition of high passenger flow are ensured by collecting the video data in real time and performing standardization processing, the accuracy of target identification and tracking is improved by combining the adjustment of the size and the position of the target detection frame, the motion path of the target is analyzed, and the mark information is added; the trend of a plurality of customers is accurately tracked and compared with feature vectors stored in a database, repeated targets are effectively distinguished, passenger flow statistics is carried out, in-out behavior analysis is carried out in combination with store layout parameters, a storefront is helped to adjust human resource allocation, and the operation efficiency and customer satisfaction are improved.

The invention discloses an application sequence prediction method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a historical use behavior sequence corresponding to a target application program; establishing a first-order state transition matrix based on the historical use behavior sequence; determining a reference application program from the historical use behavior sequence, and obtaining an application program set corresponding to the reference application program from the first-order state transition matrix; determining a reference application program sequence based on the historical use behavior sequence; and splicing the application program set with the reference application program sequence to obtain a target application program set, and determining a second number of application programs from the target application program set as an application program sequence prediction result. According to the method, the application program sequence to be used by the user is predicted by directly combining the matrix transfer algorithm and the segmentation statistical algorithm, a neural network model does not need to be introduced for prediction, and the problem of deploying the neural network model on marginalized equipment with limited computing power resources is solved.

The invention provides a construction method of a compact sandstone core natural fracture characteristic parameter characterization model. The construction method specifically comprises the following steps: step 100, self-making a compact sandstone core natural fracture image data set; step 200, establishing an efficient self-adaptive Frac-YOLOv8 rock core natural fracture segmentation network; step 300, training the network by using the data set to obtain a rock core natural fracture segmentation model; step 400, inputting a core image to be segmented into the model, and segmenting cracks; step 500, generating a binary crack data set; step 600, applying a crack skeletonization extraction algorithm to quantify the crack length; step 700, using a pixel statistical algorithm to quantify the actual area of the crack and calculating the average width of the crack; and finally, calculating the maximum width pixel value of the crack by using a crack variable circle algorithm, and quantifying the maximum width through the diameter of an inscribed circle. The method provides an effective basis for evaluation and analysis of natural rock core cracks of tight sandstone.

The present invention relates to a method, system and readable storage medium for allocating bitrate in image compression, the method comprising: evenly dividing an image to be encoded into a plurality of image blocks; using a plurality of texture complexity statistical algorithms to calculate a plurality of measurement indicators for measuring texture complexity of each image block, and fusing the plurality of measurement indicators of each image block in combination with the optimal human eye subjective evaluation result to obtain the fused texture complexity of each image block; allocating bitrate to each image block based on the encoding order of the image to be encoded, and allocating bitrate to each image block according to the maximum total bitrate under the limitation of storage space or transmission channel bandwidth and the fused texture complexity of each image block. The present invention combines the advantages of different texture complexity statistical algorithms and the optimal human eye subjective evaluation result to fuse a plurality of measurement indicators to allocate corresponding bitrates to different image blocks, and can minimize the bitrate required for compression under the premise of ensuring the subjective effect of reconstructing the image.

The invention provides a large language model evaluation method and device. The method comprises the following steps: acquiring a trained target large language model; performing attack assessment on the target large language model based on a target attack algorithm to obtain attack assessment features; and based on a target statistical algorithm or a target evaluation model, evaluating the attack evaluation feature to obtain a risk evaluation level of the target large language model.

Apparatuses, systems, program products, and method are disclosed for detecting suitability of machine learning models for datasets. An apparatus includes a training evaluation module configured to calculate a first statistical data signature for a training data set of a machine learning system using one or more predefined statistical algorithms. An apparatus includes an inference evaluation module configured to calculate a second statistical data signature for an inference data set of a machine learning system using one or more predefined statistical algorithms. An apparatus includes a score module configured to calculate a suitability score describing the suitability of a training data set to an inference data set as a function of a first and a second statistical data signature. An apparatus includes an action module configured to perform an action related to a machine learning system in response to a suitability score satisfying an unsuitability threshold.

The invention belongs to the technical field of hypersonic flight vehicle control, and relates to an intelligent control method for a hypersonic flight vehicle driven by coupling information. Firstly, a longitudinal dynamic model of the elastic hypersonic aircraft is established; then, a coupling database based on sampling analysis is constructed, different flight envelope data are simulated, a coupling degree matrix is calculated through a sampling statistical algorithm, and the coupling database is formed to quantify the mutual influence between variables; furthermore, deep neural network training driven by coupling data is carried out, a long-short term memory network is adopted, and a coupling database is used as a data sample to carry out offline training, so that the autonomous fine control capability of the aircraft in a strong coupling environment is improved. And finally, designing an online controller to realize a coupling information intelligent compensation scheme, dividing aircraft maneuvering control into a speed loop and an attitude loop, and designing a corresponding control instruction and a neural network updating law to realize real-time and intelligent flight control.

The invention discloses a multi-device collaborative passenger flow statistical method and system, and the method comprises the following steps: presetting a unique identity label and a physical position index for devices, reading the unique identity label and the physical position index, and initializing the attribute information of the devices; a user datagram protocol broadcasting mechanism is started, and heartbeat information including the unique identity label, the physical position index and the online state is sent according to a preset period; monitoring the user datagram protocol broadcast message in the local area network, constructing and maintaining an online equipment list, and updating the online state of the equipment according to the heartbeat information receiving analysis condition; judging adjacent equipment based on a physical location index, and deciding whether to start a first boundary or a second boundary according to the online state of the adjacent equipment; and the decision result is transmitted to a passenger flow statistical algorithm to generate a shop-crossing detection line. According to the invention, multi-device collaborative passenger flow statistics dynamic boundary configuration is realized, the shop-crossing detection line is accurately generated, and the passenger flow statistics problems of fixed boundary configuration, easy missed counting and poor multi-device collaboration are solved.

The invention discloses a pouring planning method and device of a material distributing machine, electronic equipment and a storage medium, and the method comprises the steps: obtaining a BIM model of a pouring site, a material distributing machine model and obstacle information of the pouring site, and obtaining a feasible region of the material distributing machine corresponding to the material distributing machine model in the pouring site according to the BIM model, the material distributing machine model and the obstacle information; according to the pouring attributes of the components contained in the BIM model, the components contained in the pouring site are divided into pouring components and poured components, communication processing is conducted according to the adjacency of the pouring components, and the area where all the pouring components are located is determined as a pouring area; according to the BIM model of the pouring area and the feasible region, based on concrete flow collapse and component connectivity, a collapse statistical algorithm is used for calculation, and a pouring planning result of the material distributing machine is generated; and according to the pouring planning result, a material distributing machine is controlled to move in the pouring site so as to conduct pouring treatment on all the areas in the pouring area. And the pouring safety and the pouring efficiency are improved.

The invention relates to the technical field of organic light-emitting device production process data processing, and provides an organic light-emitting device production process optimization method, which comprises the following steps of: monitoring key parameters in an evaporation cavity in real time, analyzing data in a cleaning treatment process by utilizing a preset statistical algorithm, and optimizing the production process of an organic light-emitting device. And extracting feature data influencing the surface cleanliness and flatness of the substrate. And then, establishing a mathematical model in the vacuum evaporation process, and solving by applying an optimization algorithm to obtain a parameter combination which enables the performance of the device to be optimal. Furthermore, electrical and optical performance and life stability data of the device are collected, and optimization parameters are simulated, calculated and optimized through a data processing model. In the production process, equipment operation data are collected in real time, optimization parameters and real-time data are compared, and the parameters are optimized in real time through a stochastic gradient descent method and stored in a knowledge base. The device performance and the production efficiency are effectively improved, and the production cost is reduced.

The invention provides a liver and gall disease prediction model construction method, system and device combined with multi-modal data and a medium, and belongs to the technical field of data prediction model construction. Medical data of a patient is collected; extracting the medical data in the medical data set, and evaluating the feature weight of each piece of medical data by using a regression analysis statistical algorithm; a comprehensive feature vector set is generated through fusion, and a preliminary liver and gall disease prediction model is constructed; and deploying the liver and gall disease prediction model to a medical terminal, and performing periodic updating through a cloud. A regression analysis statistical algorithm is utilized to evaluate a feature weight, features, such as key features such as glutamic-pyruvic transaminase and liver ultrasound image texture features, which have important influences on liver and gall disease prediction can be screened out, the data dimension is reduced, the model complexity is reduced, the key feature effect is highlighted, and the interpretability and prediction accuracy of the model are improved.

Data compression with quantum-resistant intrusion detection, that measures in real-time the probability distribution of an encoded data stream and analyzes entropy characteristics across multiple bit-scale windows to detect both classical and quantum-generated intrusions. The system compares the probability distribution to a reference probability distribution and uses statistical algorithms to determine divergence between distributions while simultaneously analyzing entropy cascade patterns characteristic of quantum computing sources. When divergence exceeds configured thresholds or quantum-generated characteristics are detected, the system generates intrusion alerts identifying the threat type. The system comprises encoding and decoding machines, an intrusion detection engine that performs multi-scale entropy analysis, a codebook training engine that creates quantum-resistant codebooks using entropy-stratified training algorithms, and databases including a quantum signature database storing compression patterns of known quantum algorithms. The codebook training engine adaptively retrains encoding algorithms upon detecting new quantum patterns, maintaining system effectiveness against evolving quantum threats.

The invention discloses a method for monitoring the stability of an ion implanter and a fault detection and classification system, and the method comprises the steps: obtaining a plurality of values of key parameters of the ion implanter at different moments in an ion implantation process, and enabling the lead-out current of the ion implanter to be stable and unchanged, and enabling the key parameters to be dynamically adjusted; performing statistics on the plurality of values by using a target statistical algorithm having correlation with the product yield of ion implantation by using the ion implanter to obtain a statistical result; and determining the stability of the ion implanter in the ion implantation process according to the statistical result. According to the invention, real-time and accurate monitoring of the stability of the ion implanter is realized.

The invention belongs to the technical field of SLAM (Simultaneous Localization and Mapping), and discloses a robot vision algorithm for a large visual angle, a storage medium and equipment, and the algorithm comprises the steps: S1, collecting image information, extracting feature information from an image through employing a multi-scale and multi-stage neural network, and repairing a fuzzy region; s2, performing position prediction on the feature points matched with the image based on an IMU sensor, and judging whether the feature points of the next frame are in a prediction range or not; s3, dividing the image into a plurality of regions, calculating the information entropy of each region, and performing grid division according to the size of the information entropy; and S4, providing a new neighbor grid domain grid statistical algorithm to further screen the feature points screened by the IMU sensor. According to the method, the blurred image frame is repaired, so that a large number of image feature points can be accurately extracted in large-view-angle motion, and timeliness and accuracy are improved in accurate matching through improvement.

The invention relates to the technical field of grid-connected monitoring data accurate checking, in particular to a grid-connected monitoring data accurate checking method, system and device based on intelligent sensor fusion and a medium. The method comprises the following steps: collecting power grid monitoring data in real time, and preprocessing the collected power grid monitoring data; fusing the power grid monitoring data to obtain fused power grid data, and filtering systematic deviation; constructing a power grid monitoring data model, and detecting data exception and positioning an exception source in combination with a statistical algorithm; checking and correcting the data subjected to anomaly detection, performing data optimization, and adjusting sensor parameters through a feedback mechanism according to the checked data; and storing the checked data in a database, and sending the data to a power grid dispatching center. Real-time detection and source positioning of abnormal data are realized, problems are found quickly, and measures are taken.

The invention discloses a fast beam forming method based on a sparse fast Fourier transform algorithm, and belongs to the field of array signal processing. The method comprises the steps of sparse fast Fourier transform, multi-channel large-value frequency point statistics, narrowband beam forming algorithm and the like. Firstly, a sparse fast Fourier transform algorithm is used for calculating the large-value frequency and the corresponding amplitude of a signal, and the frequency domain calculation speed is increased; secondly, the most suitable large-value frequency point and the corresponding amplitude are obtained through a multi-channel large-value frequency point statistical algorithm, and the influence of noise interference is reduced; and finally, based on the large-value frequency points and the corresponding amplitudes, obtaining energy of each angle by using a narrowband beam forming algorithm, and outputting the energy as a beam forming result. The method has the characteristics of high calculation speed and high stability.

The invention provides a multi-algorithm integrated quasi-real-time environment air quality abnormal data identification method, and relates to the technical field of environment data analysis. The method comprises the following steps: acquiring air index data acquired by a plurality of ambient air quality monitoring stations, and marking abnormal data; marking the air index data of which the numerical value is within the first preset range as invalid or corrected; screening single-point abnormal values; screening air index data with abnormal space consistency; screening a trend abnormal value in the air index data; screening an abnormal peak value in the air index data according to the obtained trend value; and determining normal data in the air index data. According to the method, the abnormal values of the air index data can be screened out according to the abnormal data distribution characteristics of the air index data, an integrated statistical algorithm, a time sequence analysis model and a rule algorithm, the advantages of multiple methods are fused, and the error recognition rate of local high-value bubbling is reduced through multi-index collaboration and correlation check, so that the accuracy of local high-value bubbling is improved. And the overall precision of data quality control is improved.

A method for testing device software of a device using a fuzzing algorithm. The method includes: initializing the fuzzing algorithm to detect an initial behavior of the device software of the device under test; executing a fuzzing test loop including: generating a fuzzed message from predefined message types; sending the generated, fuzzed message to the device under test to test the device software on the basis of the fuzzed message; detecting side-channel information during testing of the device software on the basis of the fuzzed message; recognizing anomalies in the side-channel information using at least one machine learning model and / or statistical algorithm; and if an anomaly is recognized, adjusting a parameter including adjusting fuzzability weights, of the initialized fuzzing algorithm and performing a next loop iteration of the fuzzing test loop; and if no anomaly is recognized, performing the next loop iteration of the fuzzing test loop.

A medical device for penetrating an anatomic structure, e.g., a bone structure, including a processing unit programmed to execute one or more statistical algorithms, e.g., Bayesian-based perforation detection algorithms, with electrical conductivity measured during penetration of an anatomic structure as an input to detect a breach condition, e.g., a spinal canal perforation, based on the measured electrical conductivity. The medical device may include a drill bit having sensing capabilities coupled to the distal end of a robot arm via a power drill unit mounted on the robot arm. The power drill unit may cease transmission of rotary motion to the drill bit upon detection of the breach condition.

Techniques for modifying application-generating code are disclosed. A system determines if a change to an application-generating code module results in a statistically significant change in the performance of applications generated by the module. The system makes the determination by comparing performance metrics of multiple applications generated by an un-changed application-generating code module to performance metrics of multiple applications generated by a changed application-generating code module. The system compares the performance metrics by generating distributions representing the respective performance metrics. The system generates the distributions by fitting the performance metrics to a statistical algorithm including an inter-application influence factor. The influence factor modifies the statistical model by converting a change in the shape of the distribution from (a) around a center of the performance metric distribution to (b) a shift of the center of the performance metric distribution toward a clustering of measured performance metric values.

The invention belongs to the technical field of industrial control, and discloses an industrial control virtual equipment resource pool construction method, system, equipment and medium, and the method comprises the steps: optimizing parameters of a transition probability and an observation probability in a time sequence data statistical algorithm according to time sequence characteristics of an industrial control scene, and extracting resource characteristics; generating a virtual resource model, and constructing a bidirectional mapping table of physical resources and virtual resources; optimizing a clustering algorithm by using a time weight factor, dynamically aggregating the time sequence metadata in combination with a streaming computing framework, and performing anomaly detection on a resource state in combination with the aggregated time sequence metadata based on an anomaly detection technology; based on a layered decoupling flattening architecture, a resource pool is divided into a resource layer, a data layer, a link layer, a technical support layer and an interface layer. According to the method, logic-physical layered decoupling of the industrial control resource pool is realized, seamless access and dynamic optimal distribution of multi-protocol equipment are supported, and high-reliability and low-delay bottom support is provided for users.

The invention provides a statistical method for custom energy consumption data of an energy management platform and a related device. The method comprises the steps of obtaining configuration information of input data and output data in a user-defined energy consumption data statistical algorithm when a statistical instruction of a user for user-defined energy consumption data is monitored; meanwhile, a template file of a user-defined energy consumption statistical algorithm is output; the template file is used for indicating a user to add a code of a user-defined energy consumption statistical algorithm on the basis of the template file to obtain a user-defined energy consumption data statistical algorithm file; and obtaining and analyzing a user-defined energy consumption data statistical algorithm file uploaded by the user, and running the user-defined energy consumption data statistical algorithm based on the configuration information of the input data to obtain corresponding output data. According to the method, the user-defined energy consumption statistical algorithm can be written through the template file when the user has a new energy consumption data statistical requirement, so that the user-defined energy consumption statistical requirement of the user is met, and the adaptability is high.

Methods, apparatus, and processor-readable storage media for enhancing web page loading using machine learning techniques are provided herein. An example computer-implemented method includes obtaining activity-related data associated with at least one user device and at least one web application during a web browsing session; generating one or more predictions of one or more web pages, associated with the at least one web application, to be sought in connection with the web browsing session by processing at least a portion of the activity-related data using one or more statistical algorithms and one or more machine learning techniques; and automatically preloading, in connection with the at least one web application, at least one of the one or more web pages for use in the web browsing session by the at least one user device.

The application provides a diversity biological collection and identification system, a high-definition camera and a microphone are mounted on a UAV platform, a plurality of groups of biological species and living habits are included in a database, the biological species and the living habits are in one-to-one correspondence, the living habits include sound characteristics, living traces, habitats and the like; a habit analysis module is used for obtaining animal traces in an image to be analyzed in an image analysis technology, a habit influence value is obtained by calculating the living trace reliability of all marked animal traces; a sound recognition module is used for obtaining animal calls or activity sounds in sound data as audio segments to be analyzed, a sound influence value is obtained by calculating the sound similarity of the marked audio to be analyzed; a comprehensive evaluation module is used for obtaining the number of biological samples to be analyzed, the habit influence value and the sound influence value, and obtaining the expected number of biological samples in a specified area by using a statistical algorithm.

The present invention relates to a gridded intelligent fusion method and system for sea surface wind fields based on multi-source satellite payloads. The method comprises: constructing a deep neural network model and combining the advantages of the D-Matrix statistical algorithm for medium and low wind speed inversion to obtain MWRI sea surface wind speed products for the entire wind speed range; performing spatiotemporal matching processing between multi-source satellite data and an EC background field to obtain the data source for the fusion analysis period; combining the EC background field with the wind speed, divergence, vorticity, and second-order derivative terms of wind speed at each point in the fused wind field to obtain a cost function; obtaining the gradient terms of each cost function within the cost function based on the cost function and summing them to obtain the function gradient; and obtaining the final fused wind field based on the cost function and the function gradient. The present invention achieves efficient fusion of sea surface wind field data from multi-source satellite payloads, and can improve the accuracy and spatiotemporal resolution of sea surface wind field data.

An iterative statistical algorithm based on a discrete-to-discrete data model with iterative coordinate descent optimization for image reconstruction from radiation measurements obtained in emission tomography, i.e. in a Positron Emission Tomography scanner, is described in this invention. The method presented here allows for the presence of the regularization as an additive term. Furthermore, this method makes it possible to control the convergence of the algorithm using a specific parameter. These improvements are due to the signals obtained regarding the given statistics of this imaging technique.

The disclosed system provides real-time detection and remediation of metric anomalies in a replenishment network. A computer system can receive, from data sources, event data, retrieve historic metric data associated with a metric of a retail environment, aggregate the event data and historic metric data into aggregated data for the metric, store the aggregated data in a cache data store, determine, based on applying a statistical algorithms model to the cached aggregated data, (i) a predicted metric value and (ii) upper and lower tolerance bounds for the metric, iteratively receive the event data, aggregate the data, and determine (i) and (ii) until (a) determining a condition is triggered to evaluate the metric or (b) expiration of a time period to which the predicted value and the upper and lower tolerances apply, and return, based on determining the condition is triggered to evaluate the metric, information about metric anomalies.

The invention relates to an intelligent statistical algorithm for mobile on-duty management based on security non-fixed places, which solves the technical problems of unscientificity, low efficiency, low real-time performance and repeated on-duty of security in a period of time, and comprises the following steps of: 1, carrying out multi-source data acquisition and preprocessing; 2, dynamic task planning modeling is completed; step 3, security equilibrium configuration calculation is carried out; 4, behavior analysis and early warning are completed; 5, dynamically adjusting the threshold value; according to the technical scheme, the problem is well solved, and the method can be used for intelligent statistics of security guard on-duty management.

This invention discloses a discrete element method, system, medium, and equipment for simulating equivalent crystalline materials, relating to the field of numerical simulation of rock mechanics. The method includes: converting granite images into binary images using a binarization method to identify various minerals within the binary images; determining the size data of various minerals in the binary images based on a particle size statistical algorithm; extracting micromechanical parameters using nanoindentation experiments combined with a trial-and-error method, and constructing multiple sets of equivalent crystalline models based on the mineral size data; dividing each set of equivalent crystalline models into multiple regions using a Brazilian splitting experiment; determining the optimal magnification for each region of each set of equivalent crystalline models by magnifying from non-critical regions to critical regions using a magnification algorithm, obtaining differentiated equivalent crystalline models; and performing Brazilian splitting numerical simulation using these differentiated equivalent crystalline models to obtain simulation results.