116 results about "Statistical analyses" patented technology

The invention relates to the technical field of computer data processing, and discloses an immigrant archive multi-dimensional information integration and intelligent query method, which comprises the following steps: collecting and standardizing multi-source heterogeneous immigrant archive data to generate to-be-processed data; based on the to-be-processed data, constructing an initial event knowledge graph containing element nodes, event nodes and relationships; performing causal relationship discovery and correction on the initial event knowledge graph, and performing mining, confidence updating and logic completion on the causal relationship between events by fusing data statistical analysis and a business process normal form learned from historical data to generate a time sequence causal graph; and receiving a natural language query request of a user, and executing query and reasoning on the time sequence causal atlas. According to the method, the business process logic hidden behind the data can be revealed, intelligent questions and answers for complex problems such as event reasons and influences are supported, and the intelligent level of query and the interpretability of results are improved.

The invention relates to the technical field of source network load storage, transportation and maintenance management, and discloses an intelligent operation and maintenance management system for a source network load storage multi-energy system, and the system comprises a data collection module which is used for collecting the real-time operation data, equipment state data and historical operation and maintenance data of the source network load storage multi-energy system, the real-time operation data comprises generating capacity, load capacity, energy storage state and energy transmission efficiency, the equipment state data comprises equipment operation parameters, fault early warning information and health degree indexes, and the data acquisition module is further used for acquiring the real-time operation data and the equipment state data of the building energy management system. Through a cross-system data integration and fusion statistical analysis and machine learning feature extraction technology, collaborative operation and maintenance of a source network load storage system and a building energy management system are realized, an operation and maintenance strategy is dynamically optimized based on key parameters such as an equipment health index and load fluctuation, and resource investment is precisely regulated and controlled in combination with an operation and maintenance cost optimization algorithm. And the total operation and maintenance cost and the fault recurrence rate are reduced.

The application discloses a virtual production-based equipment monitoring method and device, equipment, medium and product, and relates to the technical field of virtual production. The method comprises the following steps: acquiring running state data and running video data corresponding to a plurality of hardware devices respectively; matching the running state data of the hardware devices with monitoring indexes of the device types corresponding to the hardware devices, displaying the matching results between the running state data of the hardware devices and the monitoring indexes; displaying video pictures corresponding to the running video data of the hardware devices; and performing data statistical analysis on the running performance data of the hardware devices, and displaying the statistical analysis results of the running performance data in the form of a chart. The method realizes the automation of data detection of each device in the shooting field of virtual production in the hardware aspect, improves the monitoring efficiency of the hardware devices in the shooting field of virtual production, and reduces the workload of the on-site staff.

We provide the system. [Solution] A means of analyzing input sentiment data in real time using a natural language processing engine, A means of generating personalized self-care suggestions based on analyzed emotional data, A means of presenting the generated suggestions to the user, A means of anonymizing the input data and processing it statistically while protecting the user's privacy, A system that includes this.

The application provides a full node acquisition seismic record rapid field processing monitoring method, which comprises the following steps: step 1, a processing scheme is established in advance, a standardized naming process is established, and processing parameters are determined; step 2, a standardized data naming and operation management mode is designed according to the actual situation of a work area; step 3, a block observation system is established, conventional monitoring is performed, and beam merging and stacking are carried out; step 4, quantitative statistical analysis is performed, and integrated information feedback is carried out; step 5, an auxiliary trace head is set, rapid monitoring data extraction is carried out by using the auxiliary trace head, and the monitoring process is assisted; and step 6, monitoring results and a monitoring profile are obtained. The full node acquisition seismic record rapid field processing monitoring method meets the needs of rapid field construction, reduces the workload of field monitoring personnel, saves labor and cost, and accelerates the overall monitoring progress.

The invention relates to the technical field of park management and risk assessment, and provides an industrial park safety assessment method and system based on accident statistical analysis, and the method specifically comprises the steps: collecting failure data related to historical accidents, and carrying out the preprocessing of the failure data; building a historical data analysis model, carrying out the real-time safety tracking and monitoring of the entrance and exit of products in the industrial park, and evaluating and calculating the real-time safety capacity of the industrial park; and accessing the real-time safety capacity to a security sentry box monitoring background and a product transport vehicle driving navigation background, and executing a dynamic safety transport management strategy. The method solves the problems that in the prior art, the industrial park is a multi-enterprise and multi-worker dense personnel area, and in the transportation and loading and unloading process of dangerous goods in the industrial park, the dangerous goods are exposed in a high-temperature, flammable and explosive dangerous environment and a crossed area where personnel are concentrated and traffic is busy, so that the dangerous goods cannot be transported and unloaded. And the possibility that people are injured when a safety accident occurs is greatly increased.

This invention discloses a YOLO-based method for detecting surface defects on printed circuit boards, belonging to the fields of computer vision and intelligent manufacturing technology. The method includes the following steps: deploying an industrial area scan camera at the inspection station of an automated production line; when a PCB enters the inspection station, the camera is triggered by a sensor to acquire and store images; the acquired image data is processed using data cleaning, data augmentation, and target annotation methods; the processed PCB defect dataset undergoes detailed statistical analysis to optimize the model training strategy; based on the data analysis results, optimization is performed using the YOLOv5 target detection framework to establish a surface defect detection model suitable for PCB production scenarios. This invention can effectively improve the automation level and detection accuracy of defect detection, is suitable for modern large-scale batch production of PCBs, and has good engineering application prospects and industrial promotion value.

This invention discloses a phased array ultrasonic defect intelligent identification and data analysis system, relating to the field of PAUT (Polymerization and Angiography). It includes: an acquisition module for driving a phased array ultrasonic sensor array to perform weld seam scanning according to preset scanning rules, simultaneously acquiring and outputting ultrasonic echo time-series data across the entire depth range of the weld seam; and a preprocessing module for receiving the ultrasonic echo time-series data, performing data correction, noise filtering, and initial screening of effective signals, while simultaneously receiving feature anchoring parameters output by a mapping module, refining the signals in the corresponding regions, and outputting the results. This invention enables continuous, blind-zone-free scanning across the entire thickness and width range of the weld seam, simultaneously completing accurate echo signal correction and defect feature extraction, improving defect identification accuracy through closed-loop adaptive processing, accurately determining defect types and calculating their spatial location, size, and other core parameters, and simultaneously completing defect data statistical analysis and welding process correlation.

The application discloses a power grid real-time event detection and positioning method and system based on synchronous phasor data. Firstly, linear regression and Chebyshev detector are used for abnormal detection and cleaning of PMU synchronous phasor data, and a DBSCAN clustering algorithm is used for detecting clustering change points to classify events. Then, PMU data is statistically analyzed from five dimensions, and PMU scores are calculated. Finally, subgraph construction and scanning are performed to obtain the event location. The simulation results prove the effectiveness of the application in event detection, classification and positioning. The results of industrial data prove the necessity of abnormal detection and the effectiveness of the event detection algorithm. The application can provide a very effective PMU-based power grid real-time event detection tool, and can help power system operators to improve decision-making efficiency.

The invention discloses an educational performance assessment questionnaire generation and data statistical analysis method, and relates to the technical field of educational management, and the method comprises the steps: constructing a question bank, distributing educational scene attribute tags for questions in the question bank, and building a mapping relation table of keywords and tags; providing a sample confirmation interface, receiving investigation object attributes input by an organizer, and carrying out statistical analysis on the total sample quantity; calling a data interface of the school roll system, obtaining the total number of the investigated objects under each attribute dimension, calculating the sample size of each attribute, and generating a sample size distribution list; receiving keywords, question types and quantity input by an organizer, screening questions, and intelligently assembling the questions into a questionnaire; obtaining a contact way of the investigation object in the sample size distribution list, and directionally issuing a questionnaire; and recovering the questionnaire, rejecting invalid data, combining dimensions based on sample attributes selected by an organizer, calculating a question option proportion and an average score under each dimension, and generating a report. The method can be used for performance assessment scenes in the education field.

The invention discloses a full-automatic test system for a single mainboard of a high-efficiency server, and relates to the technical field of server test. Comprising a test control module, a parallel computing module, a distributed test module, an intelligent diagnosis module and a data storage and analysis module, the test control module is respectively connected with other modules and is responsible for coordinating the working time sequence of each module and controlling the whole process of the test process from starting to ending; the parallel computing module enables multiple test tasks to be carried out simultaneously in a parallel processing mode for a CPU, a memory, a memory, a network and a USB which need to be tested on a server mainboard, the distributed test module distributes the test tasks to multiple independent test nodes to achieve multi-node cooperative work, the intelligent diagnosis module integrates an intelligent algorithm, and the intelligent diagnosis module achieves multi-node cooperative work. Abnormal data generated in the testing process are automatically analyzed, problem reasons are rapidly positioned, diagnosis results are generated, data storage adopts a database to store original data, intermediate results and final testing results in the testing process, data traceability is ensured, an analysis module carries out statistical analysis on the stored data, and the analysis result is obtained. A test qualified rate report and a fault distribution report are generated, and data support is provided for production optimization.

The invention belongs to the field of wind turbine generator torque control, and particularly relates to a wind turbine generator torque control performance evaluation method and system, and the method comprises the steps: obtaining SCADA time sequence data of a wind turbine generator as a sample, obtaining sample data, cleaning the sample data, generating a torque-rotating speed curve, solving the inflection point of the curve according to the slope, carrying out the segmentation, and obtaining a torque-rotating speed curve; control parameters are obtained; performing statistical analysis on the constant power section data to obtain constant power section parameters; taking the non-constant power section control parameters and the constant power section parameters as training samples; inputting the training sample into a single-classification machine learning model for training, optimizing parameters of the single-classification machine learning model to obtain an optimized learning model and an output result, and taking the output result as a fault threshold value; and processing actual SCADA data, inputting the processed data into the optimization learning model to obtain an output result, and comparing the output result with a fault threshold to obtain an evaluation result. The torque control performance evaluation method has the effect of improving the accuracy of the torque control performance evaluation result.

The application discloses a store standard library matching system and method based on big data, and belongs to the technical field of data matching. The application collects store data of all stores in a store standard library; calculates store characteristic values based on the store data; constructs a store topology structure by taking a single store as an independent topology node and taking the store characteristic values as a correlation basis; statistically analyzes historical inventory data of the stores corresponding to the topology nodes, and establishes inventory normal characteristic benchmarks of the stores; inputs real-time inventory data in the store data, the store topology structure and the inventory normal characteristic benchmarks into an artificial intelligence model, and judges whether the real-time inventory data has cross-store string recording abnormalities; when the cross-store string recording abnormalities exist, inventory string household abnormal data is constructed; the inventory string household abnormal data is traced back to the source in combination with the store topology structure and the store characteristic values, and a tracing positioning result is obtained; and the store standard library and the inventory data are corrected according to the tracing positioning result.

The invention aims to provide a host abnormal behavior analysis method and device, and the method comprises the steps: building a recognition model according to different risk scenes, carrying out the abnormal recognition of input data, storing normal data and abnormal data, building a security baseline of a host according to the normal data, carrying out the multi-dimensional data aggregation processing, obtaining the access features of the host, and carrying out the abnormal behavior analysis of the host. And finally, outputting corresponding data statistical analysis results according to different labels. On one hand, the recognition model is established through machine learning and is periodically updated, the detection accuracy of the recognition model can be improved, on the other hand, behavior characteristics of different dimensions of host behaviors are analyzed, host behavior abnormity can be effectively found, the risk that enterprises suffer from network attacks is further reduced, and the network security is improved.

The invention discloses a store standard library matching system and method based on big data, and belongs to the technical field of data matching. The method comprises the following steps: collecting store data of all stores in a store standard library; calculating a store feature value based on the store data; constructing a store topological structure by taking a single store as an independent topological node and taking a store characteristic value as an association basis; performing statistical analysis on historical inventory data of the store corresponding to each topological node, and establishing an inventory normal feature reference of the store; inputting the real-time inventory data, the store topological structure and the inventory normal feature reference in the store data into an artificial intelligence model, and judging whether cross-store string recording abnormity exists in the real-time inventory data or not; when the cross-store missort is abnormal, constructing inventory missort abnormal data; according to the inventory missort abnormal data, tracing is carried out in combination with the store topological structure and the store characteristic value, and a tracing positioning result is obtained; and correcting the store standard library and inventory data according to the traceability positioning result.

The application relates to the technical field of battery insulation monitoring, and particularly provides a power battery insulation monitoring method, system and device, aiming to solve the problem of how to accurately predict the risk of insulation deterioration of a power battery before the power battery occurs insulation deterioration, so as to perform fault early warning on the power battery. The application performs data statistics on long-time and large-data insulation resistance values, and analyzes whether the power battery has the risk of insulation deterioration according to the data statistics result; if the power battery has the risk of insulation deterioration, an alarm information is output. Based on the data statistics and analysis on long-time and large-data insulation resistance values, the application can perform insulation deterioration prediction in advance before the power battery occurs insulation deterioration, so that the user can timely perform battery maintenance before the power battery occurs insulation deterioration, and prevent the power battery from occurring failure faults.

The invention is suitable for the technical field of contactor detection, and provides a multi-channel direct current contactor control detection system, which comprises a plurality of programmable controllers, a data switch and an industrial personal computer, each programmable controller is connected with any direct current contactor and is used for acquiring opening and closing time data of the connected direct current contactor; the data switch is connected with each programmable controller and is used for transmitting each opening and closing time data; and the industrial personal computer is connected with the data switch and is used for carrying out data statistical analysis on the opening and closing time data and determining a performance detection result of each direct-current contactor so as to realize performance comparison of each direct-current contactor. According to the technical scheme provided by the invention, accurate detection and full-period statistics of the multi-channel opening and closing time interval of the direct-current contactor are realized, and real-time detection under the same aging test condition can be carried out on multiple direct-current contactors of different models at the same time; the method is used for comparing parameters such as service life and stability of direct current contactors produced by different processes.

The invention relates to an index system dynamic construction method and device based on a CNN-Kriging model. The method is applied to a center node of a distributed intelligent spectrum access system comprising the center node and a plurality of distributed edge nodes. Comprising the following steps: preprocessing a data sample reported by a distributed edge node, dividing a preprocessing result into a training set and a test set, and training a CNN-Kriging model; a synthetic data set is generated according to the trained CNN-Kriging model; and performing statistical analysis and index optimization by adopting a global sensitivity analysis method according to the synthetic data set, updating a data sample according to an optimized index system, and updating the index system to obtain an optimized index system oriented to a spectrum access decision. According to the method, the problems of low spectrum efficiency, poor reliability and incapability of self-adaption to a dynamic environment caused by a static and uninterpretable interference management strategy under dense deployment of massive Internet of Things equipment are solved.

The application belongs to the field of intelligent manufacturing equipment application, and discloses a eucommia ulmoides flavone bitter-reducing process monitoring and early warning method based on multi-source data statistics; original raw material quality data, flavone bitter-reducing process parameter characteristics and bitter-reducing eucommia ulmoides chemical component parameter characteristics are collected, high-dimensional feature processing is performed on the original raw material quality data, the flavone bitter-reducing process parameter characteristics and the bitter-reducing eucommia ulmoides chemical component parameter characteristics to obtain eucommia ulmoides flavone bitter-reducing process monitoring characteristics, a bitter-reducing process monitoring and early warning model is constructed by using the monitoring characteristics and the eucommia ulmoides flavone bitter-reducing process monitoring and early warning method with corresponding label processing, and real-time eucommia ulmoides flavone bitter-reducing process monitoring and early warning methods are obtained by using the model processing. The application realizes comprehensive statistical analysis and early warning of multi-source data, and uses an improved machine learning model to output real-time eucommia ulmoides flavone bitter-reducing process monitoring and early warning methods. The bitter-reducing early warning is changed from traditional experience-driven to data model-driven precise early warning.

The invention relates to the technical field of sludge treatment and disposal, and discloses a sludge treatment and disposal whole-process supervision system, method, equipment and medium, and the system comprises a data statistical analysis module and a supervision module; the data statistical analysis module is used for performing statistics and analysis processing on the sludge supervision information of a plurality of target main bodies involved in the sludge treatment process to obtain a processing result; the supervision module is used for supervising the whole sludge treatment process according to the treatment results based on the multiple target main bodies to obtain a supervision result, so that the coverage supervision of the whole sludge treatment process is realized, the supervision blind area or fragmentation problem is avoided, the sludge flow direction can be accurately tracked, and the sludge treatment efficiency is improved. Various violation events including tail end stealthy discharge and stealthy transportation are effectively restrained, it is ensured that the whole sludge disposal process is compliant, and the blank that an existing supervision means is insufficient in coverage of the whole process is filled up.

The invention discloses an official document and archive management system, which relates to the field of archive management and comprises an official document downloading layer, a processing layer, a storage layer and a query layer, the official document downloading layer is used for downloading official documents in the OA system; the processing layer is used for storing all the official documents downloaded by the official document downloading layer into the storage layer in a distributed redundant storage architecture; the processing layer is further used for determining the incidence relation between the official document and the upper file and storing the incidence relation between the official document and the upper file to the storage layer; a user queries and counts the official documents stored in the storage layer through the query layer, and queries an association relationship graph of each official document; the association relationship graph of the official document is used for displaying an association relationship between the official document and the target official document; the target official document comprises an upper file and a lower file. The method integrates multiple functions of official document downloading, storage, searching, statistics and association relationship analysis.

The invention provides a multi-source heterogeneous clinical data integration platform, and relates to the technical field of medical data management systems, the platform is applied to an intelligent hospital data management system, and the platform carries out data acquisition on a plurality of databases with different business purposes of a business system layer of the intelligent hospital data management system through a data acquisition assembly according to needs; the method comprises the following steps: collecting clinical data, publishing the collected clinical data to a Kafka integration engine, performing processing, cleaning and other treatment on the clinical data through a data treatment component according to data application requirements, comparing and repairing the treated clinical data, and finally subscribing the treated clinical data to one or more data centers through a subscription component. The method is used for data statistical analysis or complex query and provides data support for smart hospital upper-level service and big data application, so that the performance problem caused by direct cross-source-end database access is eliminated, and the data value is fully utilized.

The application discloses a railway turnout identification method based on track profile point cloud data, and comprises the following steps: establishing a classification network through an intelligent service module, classifying normal profiles and suspected turnout profiles of single-track point cloud profile graphs through the classification network, and obtaining a classification model file; exporting the required onnx model file through the intelligent service tool, calling the exported onnx model to process the collected track profile point cloud data in real time, and classifying and judging the turnout rail profile; classifying the rail profile of the point cloud data of the left and right tracks, if any side of the left and right tracks is a turnout, the profile is regarded as a turnout profile; establishing a sliding window queue, the rail profile classification result of each profile is added to the queue in real time, each queue of the sliding window queue is statistically analyzed, and when there are a set proportion of turnout profiles in the sliding window, the sliding window is a turnout; hysteresis processing: at the moment when the sliding window is judged as a turnout, a window length is rolled back to determine the starting point of the turnout.

The invention provides a control method based on server resource monitoring and container migration. According to the method, a host machine resource monitoring module, a resource reporting module, a log statistical analysis module and a container migration module are arranged; the host machine resource monitoring module reports conditions through the resource reporting module according to various performance indexes of an open-source monitoring host machine, resource conditions used by a container, and internal conditions and resource use conditions of a program monitored by an Actuator of SpringBoot, and the log statistical analysis module records operation logs and analyzes and predicts use requirements of a user; and finally, a container migration module obtains actual conditions of a migrated target host machine, an original host machine and a migration mirror image through calculation according to the index condition of the host machine in combination with a user operation log and an operation requirement, dynamic migration of the service is completed, optimal utilization of host machine resources is achieved, and the service migration efficiency is improved. And various indexes of performance such as QPS, concurrency, response time and the like of service setting are achieved.

The application relates to the computer technical field and discloses a hardware consistency screening method, device, equipment and medium, which comprises the following steps: in a primary sub-cycle, fixing the phase of a data selection signal in a first double-rate synchronous dynamic random access memory (DDR); in each cycle round, selecting a delay value corresponding to each cycle round from a plurality of pre-configured delay values for a delay phase of a first data bit in the first DDR; sequentially starting and executing a continuous write command to obtain a write data code stream; after obtaining the write data code stream each time, starting and executing a continuous read command to obtain a read data code stream; then determining a minimum delay value and a maximum delay value; and then sequentially determining a first candidate delay value and a target delay value corresponding to each data bit of each DDR in the plurality of DDRs; and statistically analyzing the target delay value to determine a screening threshold. The method can improve the accuracy and reliability of data.

The invention discloses a digital meter data security consistency verification system and method. The system comprises a sample management module, a task management module, a test module, a result analysis module and a report management module. Performing sample registration and task association of the digital meter, and performing safety verification after a test number is generated; test parameter configuration is carried out, then EID verification is carried out on the digital meter, and the legality and uniqueness of the digital meter are verified; carrying out data protocol consistency verification on the wired and wireless digital meters; and carrying out statistical analysis on the detection result of the digital meter, and carrying out report management. The method is deployed in a second-level database to mainly realize synchronization with a legal EID of a first-level database, and a final detection result is synchronized to the first-level database by checking the EID of a detected digital meter and testing the consistency of meter data protocols, so that a full-link data management and control link system of the digital meter is constructed.

The invention discloses a password resource scheduling method and system based on data access mode prediction, and relates to the technical field of password resource scheduling, and the method comprises the steps: obtaining a data access historical record, and carrying out the statistical analysis of the data access historical record through a sliding window, and obtaining an access frequency gradient and an access interval gradient; mutation feature detection is carried out according to the access frequency gradient and the access interval gradient, and a phase change critical point of a data access mode is obtained; calculating a change rate of a data access mode gradient based on the phase change critical point and generating a scheduling inertia compensation parameter; and performing predictive scheduling on the password resources according to the scheduling inertia compensation parameter and the phase change critical point, obtaining a deviation between an actual scheduling effect and a predictive scheduling effect, and adjusting the sudden change feature detection according to the deviation. The password resource preheating process can be actively started before phase change occurs, scheduling response delay is reduced, and resource utilization efficiency is improved.

The invention relates to a biological detection data analysis method, which belongs to the technical field of data analysis, and comprises the following steps: obtaining inspection data, generating a unique sample number, calling a standard template and matching a standard detection item, generating a sample preparation guide, receiving a triggered preparation confirmation instruction, obtaining a sample preparation completion instruction, and triggering a sample detection instruction. The method comprises the following steps: acquiring original data of equipment, judging according to a judgment standard to obtain a detection result, calling a multi-dimensional data analysis engine to perform statistical analysis on the detection result based on the detection result, and calculating an epidemic disease positive rate and comparing the epidemic disease positive rate with a preset positive rate reference range to obtain an analysis result, so that intelligent upgrading in the field of biological detection is realized; the traceability and the processing normalization of sample information are ensured, the problems of large personal error and data analysis lagging in the traditional detection process are effectively solved, and the comprehensive purpose of improving the detection accuracy, the detection efficiency and the risk early warning capability is achieved.

The application provides a cloud computer recommendation method and system applied to mobile office. A cloud server acquires historical record information of mobile device accessing a cloud computer, acquires a current geographic position of a user mobile device, acquires all available cloud computers existing in the geographic position according to geographic position analysis, calculates a maximum likelihood P1 of a cloud computer ID / name existing in the current geographic position being accessed by an outsider, statistically analyzes a probability P2 of each cloud computer ID / name accessed by the user using the mobile device, matches a corresponding weight, and obtains a ranking of the cloud computer ID / name most likely to be used by the user for selection and switching. Through the method, the cloud server can predict different scene cloud computers required to be accessed by the mobile device user through information analysis, thereby achieving active provision of use prediction and intelligent recommendation, switching guidance service for the user accessing the cloud computer.

The application discloses a regional recycling economy operation state monitoring and evaluation method and system based on an Internet of Things, relates to the field of data analysis, and comprises a collection module, a garbage recycling related information collection module is used for collecting target area garbage recycling related information, and the garbage recycling related information is counted; an analysis module is used for receiving the garbage recycling related information counted in the collection module, and analyzing the garbage treatment tendency based on the garbage recycling related information; the application collects multi-dimensional data such as garbage properties and weights accurately, constructs a treatment tendency model in combination with parameters such as moisture content, can scientifically judge the more suitable treatment path of garbage incineration or landfill, simultaneously introduces a methane production decay trend evaluation mechanism, relies on a matrix type deployed sensor to monitor the landfill gas production state in real time, combines a weight algorithm to dynamically evaluate the gas production health degree, and provides double data support for garbage allocation strategy.