94 results about "Statistical algorithm" patented technology

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 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 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.

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.

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.

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.

A system and method for filesystem data compression using codebooks, that measures in real-time the probability distribution of an encoded data stream, compares the probability distribution to a reference probability distribution, and uses one or more statistical algorithms to determine the divergence between the two sets of probability distributions to determine if an unusual distribution is the result of a data intrusion. The system comprises both encoding and decoding machines, an intrusion detection module, a codebook training module, and various databases which perform various analyses on encoded data streams. Further, the system comprises a system for integrating the compression into a filesystem for both system-wide compression on a per-file or filegroup basis, and intrusion or alteration detection of files.

The present invention relates to the fields of bridge engineering and signal processing technology, and in particular to a method and application for fully automatic cable frequency extraction, order determination, and cable force identification. The present invention first uses a sensor to obtain time-course signals such as acceleration and displacement of cable vibration, and uses Fourier transform and other methods to obtain the spectrum of the vibration signal; then uses a memory-type sieve peak algorithm to search for peaks in the spectrum, that is, the modal frequency of the cable; hash voting is performed on all acquired frequency peaks to obtain the nominal fundamental frequency, and then the order corresponding to all picked frequencies is determined; after obtaining all frequencies and orders, the cable-beam vibration theory can be used to obtain parameters such as cable force. The present invention combines the memory-type sieve peak algorithm and the hash voting statistical algorithm to achieve a fully automatic and intervention-free cable force identification process, and has strong engineering applicability.

A method for determining a convolution kernel for an iterative statistical algorithm based on a continuous-to-continuous data model. The method is used for image reconstruction from radiation measurements obtained in emission tomography, specifically in a Time-of-Flight Positron Emission Tomography (TOF PET) scanner. The presented method is a non-list-mode solution that reduces the computational complexity of the reconstruction problem, improves the resolution of reconstructed images, reduces the radiation dose absorbed by patients during TOF PET examinations, and / or shortens the measurement acquisition time without significantly compromising the quality of the diagnostic images. Notably, the quality of the functional images remains at the same level. These improvements are achieved by designing the convolution kernel to account for the statistical properties of the measurement signals registered during the TOF PET scanner's acquisition process.

The invention relates to the technical field of constructional engineering informatization, and discloses a steel bar optimized blanking method and system based on construction specification constraint and dynamic planning, and the method comprises the steps: collecting multi-dimensional feature data, and organizing the multi-dimensional feature data into a graph structure; performing dimension reduction and hidden variable learning on the high-dimensional joint feature data by using a variational auto-encoder, and constructing a joint performance evolution probability generation model in a submerged space; capturing a complex dependency relationship through a time sequence attention module and a graph convolution module; constructing a life distribution function by applying a Monte Carlo statistical algorithm; a particle swarm algorithm is combined with a chaos disturbance exploration cutting scheme, and an optimal cutting strategy is solved through a dynamic programming algorithm; according to the method, multi-dimensional feature fusion representation, long-term performance prediction precision improvement, full-life-cycle optimization decision and engineering specification conformity guarantee are realized, and scientific and reliable decision support is provided for large-scale infrastructure engineering reinforcement construction.

The application relates to a multi-modal data governance method, device and equipment based on multi-agent cooperation. The method comprises the following steps: identifying the variable mode of a variable in to-be-governed data; converting mixed-mode variables into text or numerical-mode variables by using a mixed data agent; performing standardization evaluation, standard term rewriting and material type division on the text-type variable by using a text agent; dividing the numerical-type variable into a continuous type, an unordered discrete type or an ordered discrete type based on variable semantics and value characteristics by using a numerical agent; and performing corresponding statistical algorithm governance operations according to the divided material types. By using the method, semantic-level recognition and conversion of multi-modal data can be realized through multi-agent cooperation, governance strategies can be accurately matched based on fine statistical attribute classification, and the accuracy and automation efficiency of data governance can be significantly improved.

This invention relates to a field weed infestation early warning method based on weed density detection: First, the operating coordinates of a rice transplanter are obtained, and the transplanter's operating trajectory is constructed. Then, the mechanical characteristics of the transplanter's implements are used to locate the theoretical seedling coordinates. Next, the spacing of the actual seedling geographical coordinates is used to construct a theoretical seedling circle region with the theoretical seedling geographical coordinates as the origin and a preset distance as the radius. Then, images of the farmland after transplanting are collected using a drone, and a deep learning target detection model is constructed. A computational mapping model is constructed to map the theoretical seedling circle region onto the farmland image. A plant quantity statistics algorithm model is constructed to perform quantity statistics calculations on the plants detected by the target detection algorithm in the farmland image. Target detection processing and plant quantity statistics are performed on the farmland images outside the theoretical seedling circle region. A weed infestation early warning threshold is set; exceeding this threshold indicates severe weed infestation, requiring an early warning.

The invention discloses a model training data processing method and system based on an improved YOLO algorithm. The method relates to the technical field of YOLO algorithm data processing, and comprises the steps of data conversion and original data configuration, PC end prediction recognition model generation, embedded end prediction recognition model generation and embedded end prediction recognition model updating. The method comprises the following steps: extracting a predefined data set to identify and count original parameters configured by a YOLO algorithm; configuring initial parameters according to a YOLO algorithm, and inputting the initial parameters into a training script for data training; converting the PC end prediction identification model into an embedded application environment model; and configuring initial parameters according to the updated YOLO algorithm to reconfigure the embedded end prediction identification model. The effect of improving the application extension adaptability of the YOLO model on the embedded platform is achieved, and the problem that in the prior art, the application extension adaptability of model training data processing of a YOLO algorithm on the embedded platform is insufficient is solved.

The invention provides a WEB system upgrading method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of computers. The upgrading method comprises the following steps: acquiring a target report component and an online type thereof, and judging whether the online type is trial online or not; in response to the online type being trial online, determining a trial user of the target report component based on a user portrait algorithm, the user portrait algorithm including one of a data statistical algorithm and a clustering algorithm; obtaining trial data and a feedback result of the trial user, and judging whether the trial data and the feedback result meet a preset condition or not; and in response to the trial data and the feedback result meeting a preset condition, the target report component is online to upgrade the WEB system. The problems that server resources are seriously wasted, project cost is high, a data synchronization mechanism is not perfect enough, and data are prone to being lost and repeated in the prior art are at least solved. The method is suitable for system upgrading and updating scenes.

The application provides a method for tracking and counting oil pipe materials, which comprises the following steps: S1, preprocessing and data enhancement processing are performed on pre-acquired images of different types of oil pipe materials, and an oil pipe material target detection model for detecting oil pipe materials is constructed and trained; S2, the oil pipe materials in an oil pipe material video are identified through the oil pipe material target detection model, and each oil pipe material is given a unique ID tag according to the identification result, and the oil pipe materials are counted and tracked according to the ID tags by using a multi-frame cooperative unified calculation algorithm. The application also provides an electronic device. The application gives each oil pipe material a unique ID tag by using a target tracking algorithm, realizes accurate detection and tracking of oil pipe materials, and effectively solves the situation that a single frame of picture cannot contain all oil pipe materials laid on the ground by using a multi-frame cooperative unified calculation algorithm, realizes accurate counting of oil pipe materials, and is more in line with actual application requirements.

The invention provides a statistical process control method and system based on a large language model. The system comprises the large language model and a statistical process control model. Decoupling the statistical process control model and the large language model on the architecture; the large language model is used for receiving an analysis request input by a user; reasoning the analysis request, and determining an analysis intention and an input parameter of the user; according to the analysis intention, a predefined Schema parameter mode of a target analysis tool and an input parameter, generating a tool calling instruction for the target analysis tool in the statistical process control model; sending the tool calling instruction to a statistical process control model, and receiving returned analysis data determined based on a statistical algorithm library; and analyzing the analysis data, generating an analysis result in a natural language form, and feeding back the analysis result to the user. In this way, the problems that in the prior art, a statistical process control analysis tool is complex in operation process, high in requirement for professional knowledge of a user, low in use efficiency and unreliable in calculation result of a universal AI model can be solved.

The invention discloses a double-granularity drug recommendation method (LLM-CIDGMed) based on large language model driven causal reasoning, which is used for solving the key limitation of the existing drug recommendation system in the aspects of medical knowledge driven causal reasoning, patient perception multi-granularity drug characterization fusion and long sequence time sequence modeling. The method aims at solving the problems that a traditional statistical algorithm lacks deep understanding of medical field knowledge, drug representation learning lacks a multi-granularity information integration mechanism perceived by a patient, and a traditional time sequence modeling method loses long-term historical information of the patient. Experimental results show that the method is remarkably superior to an existing method in the aspects of accuracy, safety and calculation efficiency, and more reliable intelligent support is provided for clinical decision making.

The application provides a multi-dimensional cognitive state vector simulation agent digital textbook dynamic adaptability evaluation method, and relates to the technical field of artificial intelligence assisted education. The method comprises the following steps: extracting teaching entities and semantic relationships between the entities in a source file of a digital textbook to be evaluated through a multi-modal analysis model, and constructing a knowledge graph; initializing multiple groups of virtual learning agents based on a pre-trained large language model, and configuring differentiated cognitive state vectors for each group of virtual learning agents; driving each group of virtual learning agents to traverse the teaching knowledge graph according to a topological sorting by using a knowledge boundary mask mechanism, obtaining understanding feedback of each node by each group of virtual learning agents, and generating confusion degrees and thought chains in the understanding feedback process; calculating a cognitive block index and an adaptability distribution curve of the source file of the digital textbook by using a weighted summation algorithm, and generating an evaluation report. The application simulates human cognitive processes by using a large language model agent, and evaluates the quality and adaptability of a digital textbook.

This invention provides a method, apparatus, device, and storage medium for inferring a single-sample ncRNA regulatory network, relating to the field of gene identification technology. The specific implementation includes: acquiring ncRNA and target gene transcriptome data of a matched sample; for each target sample in the matched sample, calculating a first regulatory relationship strength matrix and a second regulatory relationship strength matrix of ncRNA and target gene before and after removing the target sample using a preset statistical algorithm; obtaining the fusion regulatory relationship strength matrix of ncRNA and target gene corresponding to the target sample based on the first and second regulatory relationship strength matrices; and inferring the single-sample ncRNA regulatory network of the target sample based on the fusion regulatory relationship strength matrix of ncRNA and target gene corresponding to the target sample. This invention can reflect the regulatory relationship strength between ncRNA and target gene at the single-sample level by constructing statistical correlation values, thereby inferring the single-sample ncRNA regulatory network.

The invention relates to the technical field of data processing and anomaly detection, in particular to an anomaly detection method and device, electronic equipment and a storage medium. Statistical characteristics of a historical data sequence are extracted, and a target algorithm is automatically selected from multiple candidate statistical algorithms according to the statistical characteristics; and zero manual intervention and dynamic adaptive selection of the anomaly detection algorithm are realized. The method has the beneficial effects that the optimal algorithm can be automatically matched according to the data distribution characteristics of the indexes, the limitation of manual adjustment participating in a fixed threshold value is thoroughly eliminated, the accuracy, robustness and operation and maintenance efficiency of anomaly detection are remarkably improved, and a universal solution is provided for automatic monitoring of large-scale and multi-type business indexes.

The invention relates to the technical field of data processing, and discloses a wind power project maximum wave height prediction method and system based on a space-time extremum model. The method comprises the following steps: standardizing ocean wave observation data, wind field data and topographic data through a multi-source data fusion device to obtain an ocean environment basic data set; performing parameter extraction processing through a wave spectrum analyzer to obtain a wave characteristic parameter group; inputting into a space-time correlation calculation unit to obtain a space-time correlation length scale; extreme value wave height distribution parameters are obtained through a Fedele extreme value statistical algorithm; inputting into a wave field deterministic reconstructor to obtain a deterministic wave space-time sequence; and obtaining a maximum wave height prediction result of the wind power project through a wind power structure load calculator. The problem that the wind power project extreme value wave height prediction precision is insufficient due to the fact that the wave field space-time correlation cannot be accurately considered in the prior art is solved.

The application discloses a power system probabilistic electromagnetic transient simulation method and a related device thereof, and the system comprises the following modules: a ring oscillator entropy source array module samples an oscillator output through a slow sampling clock to obtain an original random bit stream; a post-processing module performs deviation removal and whitening processing on the original random bit stream; a probability distribution transformation module transforms uniformly distributed random parameters into target probability distribution random parameters conforming to characteristics of various random factors of a power system; a parameter distribution interconnection network module distributes the random parameters to FPGA parallel simulation processing units for electromagnetic transient simulation; and a hardware statistical processing module executes a pipelined online statistical algorithm on simulation results to generate probability distribution characteristics and confidence intervals of various electrical quantities. The application utilizes internal physical noise sources of an FPGA to construct a true random number generator, thereby solving the problem of insufficient randomness quality of a software pseudo-random number generator; and a parallel simulation array is constructed on the FPGA, thereby improving simulation efficiency.