1931results about How to "Accurate judgment" patented technology

The invention discloses an intelligent security and protection monitoring method and system based on image recognition. The method comprises the following steps: S1, acquiring multi-modal data; s2, performing preprocessing to generate complete target information; s3, performing real-time detection on the generated complete target information, and performing primary processing on a conventional scene; s4, based on an improved visual SLAM technology, obtaining three-dimensional information of multiple cameras; s5, when the edge device detects a potential abnormal condition, performing abnormal behavior analysis on the data by using a Swin Transform model, and optimizing a detection result in combination with the three-dimensional information of the multiple cameras; s6, performing adaptive learning on historical data by using a depth deterministic strategy gradient algorithm, and dynamically adjusting a threshold value of abnormal behavior detection; and S7, after an abnormal condition is detected, processing data through differential privacy protection and an end-to-end encryption technology, automatically controlling security and protection equipment, and giving a real-time alarm. According to the invention, image recognition and deep learning technologies are fully utilized, and accurate real-time monitoring in a complex scene is realized.

The invention discloses a fatigue driving monitoring and early warning method based on multi-dimensional feature fusion, and relates to the field of intelligent traffic and safe driving, and the method comprises the steps: carrying out the feature extraction of a collected face image of a vehicle driver through a feature extraction model based on a convolutional neural network, and obtaining a face key point; then calculating the eye closing rate, the mouth opening rate, the head posture data and the facial muscle data to finally obtain multi-dimensional features, and fusing the multi-dimensional features by using a multi-layer perceptron to obtain fused facial features; and then, a fatigue state detection model obtained based on recurrent neural network training is utilized to detect the facial feature time sequence at a plurality of continuous sampling moments so as to detect whether a fatigue driving state occurs and trigger alarm feedback. According to the method, the fatigue state of the driver can be identified and judged more accurately through combination of multi-dimensional data fusion and a time sequence analysis algorithm, and the monitoring and early warning accuracy and reliability are improved. And the system can keep high robustness even in an illumination change or complex driving environment.

The invention discloses an intelligent navigation system and method for a ship in the technical field of ship navigation, and the system comprises a sensing module which is used for collecting the surrounding environment information and the state information of the ship in real time; the data processing and fusing module is used for preprocessing, calibrating and fusing the surrounding environment information and the self state information of the sensing module; the route optimization module is used for formulating a navigation plan and a collision avoidance strategy; the navigation control module is used for autonomously controlling the ship according to the output of the ship route optimization module; and the communication monitoring module monitors the navigation state of the ship and the change of the surrounding environment through real-time communication between the ship and a shore-based control center, other ships and a maritime affair management department. According to the method, data of different sensing units are associated, and the associated data are fused, so that mutual verification and complementation can be realized, and the accuracy and reliability of target identification are improved.

The invention discloses a system for realizing crop intelligent identification based on a CLIP multi-mode method, and belongs to the technical field of crop identification, and the method specifically comprises the steps: collecting real-time images, text description and environment data of crops; performing image processing on the real-time image; a CLIP model is used for carrying out feature extraction on the image and text description, visual features are extracted from the image, semantic features are extracted from the text, the text description comprises crop types and symptom description of diseases and pests, the environment data and the visual features are fused, and a multi-modal feature vector is generated; the multi-modal feature vectors are classified by using a pre-trained classifier, specific crop types and pest and disease damage types are identified according to extracted multi-modal features, identification results are displayed to users through a visual interface, and information is fed back to a monitoring center; according to the method, visual, semantic and environmental features are fused, so that the accuracy of crop disease and insect pest recognition is remarkably improved.

The invention provides a deep and large foundation pit monitoring method and system, a program product and a storage medium, and relates to the technical field of foundation pit detection. The GPS positioning device collects foundation pit slope crest displacement data, the monitoring sensor network collects deep horizontal displacement data, the two data types complement each other, comprehensive information of surface and internal deformation of the foundation pit is provided, and the generated deep horizontal displacement curve chart visually displays the displacement conditions of the supporting structure at all depths. A potential weak area can be identified, a foundation pit excavation response data acquisition device is arranged at the maximum stress part, response data of a key position is obtained, and the quantitative index of overall deformation is provided by measuring and monitoring the position change of a central point of a sensor network and calculating the deformation amount of the foundation pit. According to the multi-dimensional and multi-type data acquisition and integration method, the description comprehensiveness of the foundation pit environment is improved, potential risks can be judged more accurately, and limitation caused by a single data type is overcome.

The invention relates to the technical field of mold management, in particular to a mold abnormity management system and method based on machine vision, and the system comprises a mold abnormity detection module, a fault prediction and health management module, an intelligent repair and maintenance scheduling module, and a data analysis and visualization module. The mold anomaly detection module collects data by using a high-definition camera and a thermal infrared imager, and identifies surface defects through a convolutional neural network; the fault prediction and health management module predicts a fault risk and performs early warning by means of time sequence analysis and a machine learning technology; the intelligent repair and maintenance scheduling module optimizes a repair strategy and task allocation by using deep reinforcement learning and a genetic algorithm; and the data analysis and visualization module displays data by means of Grafana and Kibana and realizes intelligent alarm and decision support. According to the invention, accurate detection of mold abnormity, effective prediction of faults, optimization of maintenance scheduling and efficient processing and visual display of data are realized, and the intelligent level of mold management is improved.

The invention discloses a primary helium fan health state evaluation system and method based on state estimation, and relates to the technical field of nuclear power station equipment state monitoring and health management, and the system comprises a data acquisition unit, a feature extraction unit, a state estimation unit and a health evaluation unit. According to the system, strategies of multi-sensor data fusion, mechanism data hybrid modeling, active incremental learning and the like are adopted, and an end-to-end health state evaluation model is constructed; and the block chain technology is introduced to realize data security sharing and trusted computing. According to the system, multi-source heterogeneous data can be comprehensively utilized, the health state of the main helium fan can be automatically and accurately evaluated in real time, and a reliable basis is provided for intelligent operation and maintenance decision making. The safety and economical efficiency of the nuclear power station are improved, and intelligent development of nuclear power equipment is promoted.

The invention discloses a solid state disk testing method and system, a storage medium and a program product, and relates to the field of computer storage testing, and the method comprises the following steps: dividing a storage space of a solid state disk to be tested into a plurality of physical areas; determining the number of read-write tests of the to-be-tested solid state disk within a preset duration at a preset temperature; performing read-write test operation on the plurality of physical areas in sequence within a preset duration to obtain read-write error rates respectively corresponding to the plurality of physical areas, the number of read-write times of the read-write test operation being the number of read-write test times; if the read-write error rates corresponding to the physical areas exceeding the preset number are higher than a preset error rate threshold value, determining that the to-be-tested solid state disk is unqualified; and if the read-write error rates corresponding to the physical areas lower than the preset number are lower than a preset error rate threshold, determining that the to-be-tested solid state disk is qualified. By implementing the method, the specific problem area in the solid state disk can be positioned and diagnosed more accurately, so that the quality evaluation standard and the maintenance efficiency of the whole solid state disk are improved.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

According to the dynamic stress analysis system and method for the fabricated beam string structure, the strain, vibration, displacement, temperature, soil pressure and construction load of the structure are comprehensively monitored by integrating various sensors, the safety of data is ensured through encryption transmission, sensitive data are protected by adopting a role-based access control mechanism, and the reliability of the system is improved. In the data processing process, a stress prediction model is established by cleaning, calibrating and synchronizing data, extracting key vibration characteristics, considering temperature influence and using finite element analysis to simulate structural response, and is used for identifying a potential damage area and predicting the residual service life of the structure, so that maintenance measures can be taken in time, the safety of the structure is ensured, and the service life of the structure is prolonged. And the health management efficiency and accuracy of the fabricated beam string structure are improved.

The invention relates to the related field of photovoltaic construction, in particular to a photovoltaic construction management system, which comprehensively analyzes video data and environmental sensor data, improves the accuracy and reliability of the monitoring effect, and can effectively reduce the blind area and false alarm rate of a single data source through the fusion of multi-modal data. According to the invention, a genetic algorithm and a decision tree algorithm are integrated, so that the system has strong learning ability, and the system can be continuously self-optimized according to historical data and operation logic to gradually adapt to different construction environments and working conditions; through regular updating and model training, the system can continuously improve the monitoring precision and the safety management level, and it is ensured that an efficient and stable operation state is always kept in long-term use; an analysis result is displayed through a visual platform, accurate defect positions and severity degree marks are provided, a detailed detection report can be automatically generated, long-term monitoring and data comparison are supported, and powerful technical support is provided for construction quality guarantee.

The invention relates to the technical field of television image communication, in particular to a user information input method and system in an intelligent television all-in-one machine. The method comprises the following steps: collecting a user image, carrying out white balance correction and focusing processing, generating a moving object focusing image, carrying out identification and attention tendency analysis on a character in the focusing image, obtaining character tendency data, analyzing a historical interaction image, identifying an interaction mode and constructing a user intention prediction model, and obtaining a user intention prediction result. And when the tendency data shows that the user preferences the television content, intention drilling and auxiliary adjustment are performed on the current image based on the prediction model, predicted user input information is generated, logic conflicts in the predicted information are detected, feedback optimization is performed, and final user input information is output. According to the invention, a more accurate and more flexible user information input method is realized.

The invention discloses an industrial internet security situation assessment system based on deep learning, and relates to the technical field of industrial internet security, which comprises a data acquisition module, a data preprocessing module, a feature extraction module, a deep learning model training module and a data storage module, the system has the advantages that the deep learning model training module adopts a combined architecture and an optimization algorithm to train an accurate model, the real-time monitoring module monitors real-time data in the industrial internet according to received data, abnormal conditions are found in time, and the real-time monitoring module monitors the real-time data in the industrial internet according to the real-time data. The method comprises the following steps of: acquiring the security situation value data of the current industrial internet, transmitting the security situation value data of the current industrial internet to a situation awareness module, and performing deep quantitative evaluation and trend prediction by the situation awareness module by using a fuzzy logic and evidence theory fusion algorithm and integrating historical and real-time data to accurately calculate a comprehensive security situation value so as to calculate a more accurate comprehensive security situation value. And accurate judgment and timely and effective response to the security situation are ensured.

The invention discloses a data prediction method based on a kernel regression primary function Bayesian dynamic linear model, and belongs to the technical field of bridge structure health prediction. A to-be-analyzed bridge is detected by using a structural health monitoring system (SHM), and a time series dynamic model KR-BDLM of a Bayesian dynamic linear model is established to describe a bridge structure strain response changing along with time, so that bridge structure health monitoring data prediction and missing value filling are realized; performing parameter optimization reasoning on parameters of the KR-BDLM model by adopting a maximum likelihood algorithm; providing a time-varying state parameter and an observation prediction value of a bridge structure through the optimized KR-BDLM model, and further realizing SHM monitoring data single-step prediction; and multi-step prediction is carried out on the basis of single-step prediction, so that longer-time prediction of bridge data is realized. The prediction result is high in precision, the engineering structure safety can be effectively guaranteed, and the leading-edge development of the structure health monitoring field is promoted.

The invention relates to the technical field of three-phase current data processing, in particular to an electric energy quality intelligent monitoring method and system for a four-fusion integrated terminal. The method comprises the following steps: acquiring a current sequence and a monitoring phase corresponding to each phase line; calculating the suspected abnormal degree of the current monitoring point in the current sequence of the monitoring phase, and judging whether the current monitoring point is an abnormal monitoring point or not; if the current monitoring point is an abnormal monitoring point, acquiring a continuation curve; calculating a continuation distribution characteristic value of the abnormal monitoring point according to each data point in the continuation curve; correcting the continuation distribution characteristic value based on the distance between trough points of the crest of the continuation curve and the height of the crest to obtain a corrected continuation distribution characteristic value; obtaining the current abnormal degree of the abnormal monitoring point based on the suspected abnormal degree of the abnormal monitoring point and the corrected continued distribution characteristic value; and judging whether the electric energy quality is abnormal according to the current abnormal degree. According to the invention, the electric energy quality of the four-fusible integrated terminal can be accurately monitored.

The invention relates to the technical field of information, and discloses a parking lot panoramic safety monitoring method and system, and the method comprises the steps: obtaining a real-time video stream of a parking lot, and carrying out the denoising preprocessing of each frame of image, and obtaining a clear image; adjusting the brightness and the contrast ratio of the clear image by adopting a self-adaptive illumination compensation algorithm to obtain an illumination compensation image; according to the angle and distance change of the vehicle, correcting the vehicle image visual angle in the enhanced image by adopting a perspective transformation algorithm to obtain a perspective transformation image; identifying a vehicle position and a bounding box in the perspective transformation image by adopting a vehicle detection model based on deep learning, and determining a specific position of the vehicle in the image; and in the vehicle dense area, a multi-target tracking algorithm is combined with the moving tracks and the feature information of the vehicles, the shielding relation between the vehicles is judged, and the positions of the shielded vehicles are determined. The method can realize panoramic safety monitoring of the parking lot.

The invention provides a strip steel surface defect detection method and system based on a depth map, and the method comprises the steps: S1, carrying out data collection, obtaining steel plate surface point cloud data, and then generating a point cloud data set; s2, after a point cloud data set is obtained, preprocessing is carried out, and defect detection is carried out; s3, judging the category of the defect image; and S4, respectively displaying the defects of the steel plate in combination with detection results. Through the man-machine interaction module provided by the invention, detection results including depth, size, proportion and category conclusions of defects can be visually displayed to a user, so that the user can conveniently know the defect condition of the surface of the steel plate.

The invention relates to the technical field of disaster power grid resource allocation, in particular to a power distribution network multi-dimensional planning method and system considering extreme natural disasters, and aims to solve the problem of how to consider the fluctuation condition of the working state of a working cable before the extreme weather comes. In order to solve the problem, the planning method provided by the invention comprises the following steps: storing a power supply load curve of a working cable within a target time; according to the power supply load curve of the reference workday, obtaining the loss condition of the working cable in the extreme weather, and according to the loss condition, calculating an early warning threshold value of the working cable in various extreme weathers; calculating an extreme influence value corresponding to each extreme weather according to the weather forecast, and recording the duration of each extreme weather as an extreme duration; and comparing the extreme influence value with an early warning threshold value, and calculating the risk probability of the fault of the working cable.

The invention discloses a transformer intelligent temperature monitoring device based on a monitoring technology, which relates to the technical field of transformer intelligent temperature monitoring and comprises a temperature sensing module, a data processing module, a fault diagnosis module, a data transmission module and a remote monitoring module. And the temperature sensing module is used for acquiring temperature information of each key part of the transformer in real time. According to the invention, high-precision temperature monitoring is realized through the fiber grating sensor, the anti-interference capability is enhanced, and accurate fault diagnosis is realized through the GRU neural network and the fuzzy logic algorithm. And a dual-channel redundancy communication mechanism is adopted to ensure the stability and safety of data transmission. A Web-based remote monitoring platform improves the operation convenience, and a multi-level alarm function ensures safe operation. The system integration module realizes seamless connection with the existing power monitoring platform, has high adaptability and expansibility, reduces the deployment cost, and improves the equipment management efficiency.

The invention belongs to the technical field of aging state evaluation, and discloses a high-voltage dry-type bushing insulation aging state evaluation method based on frequency domain characteristics. According to the method, the FDS is adopted, and the aging state of the insulating material is evaluated according to the change of the complex capacitance, the complex dielectric constant, the dielectric loss factor and the like of a medium along with the frequency under a low-voltage sine alternating electric field. The FDS shows remarkable superiority in a plurality of aspects. The method comprises the following steps: firstly, measuring a dielectric constant and dielectric loss of an insulating medium in a wide frequency range by an FDS method so as to obtain response characteristics of the insulating material under different frequencies; according to the method, the frequency domain dielectric spectroscopy FDS is combined with the advantages of wide frequency range measurement, frequency domain feature recognition, comprehensive reflection of electrical characteristics and the like, so that accurate evaluation of the insulation aging state of the high-voltage dry-type bushing is realized. The safety and the stability of a power system can be improved, and faults and accidents caused by insulation aging can be reduced.

The invention, which relates to the technical field of scientific and technological achievement management, discloses a blockchain-based scientific and technological achievement authenticity verification and evaluation platform comprising an uploading module, a preliminary screening module, a verification module, a distribution module and a comprehensive evaluation module. The uploading module is used for providing an interface and a plurality of uploading modes, optimizing an uploading interface to ensure stable and efficient uploading of files of different scales, and comparing a file hash value with a pre-stored standard value by using a hash algorithm to judge the integrity of the file so as to ensure the accuracy and completeness of the uploaded file; according to the system, the authenticity and reliability of data are ensured through multiple technical means, the file integrity is ensured by the uploading module through a Hash algorithm, the material effectiveness is judged in an auxiliary manner by the preliminary screening module through a natural language and an image recognition technology, the data source is traced and cross-validated based on the block chain by the verification module, and the data problem is effectively avoided through the multi-link synergistic effect. A solid foundation is provided for subsequent evaluation, and the data credibility and accuracy are enhanced.

The invention discloses an emergency ICU ward monitoring data processing system based on big data, and relates to the technical field of big data processing, the monitoring data processing system comprises a data collection module, an abnormity marking module, a real-time data analysis module, an abnormity evaluation module and an early warning information generation module, and the modules are in electric signal connection. According to the emergency ICU ward monitoring data processing system based on big data, by analyzing vital sign data of a patient and equipment operation data, once abnormal fluctuation is found, early warning information is immediately generated and sent to medical staff, it can be ensured that the medical staff know the abnormal condition of the patient in the first time, so that processing measures are rapidly taken, and the medical staff are prevented from being damaged. Compared with a traditional manual monitoring mode, the system has the advantages that the response time is greatly shortened, the first-aid efficiency is improved, and meanwhile, the system can also provide detailed monitoring data and analysis reports to help medical staff to judge the illness state more accurately and formulate a more accurate treatment scheme.

The invention relates to the technical field of power supply monitoring, in particular to a power supply monitoring system with a hierarchical authority encryption function, which comprises an edge calculation processing module for reading voltage, current and load data based on a power supply monitoring node, calculating voltage fluctuation in sections, monitoring current change in real time, comparing amplitude and screening a sudden change value; load balance and abnormity are judged, and power node fluctuation characteristics are generated. According to the method, the current and voltage signal characteristics are analyzed step by step by adopting the mode matching algorithm, the power abnormal event is accurately captured, the identification efficiency of the fault event is effectively improved, the false alarm and missing report conditions are reduced, and the accuracy and timeliness of power monitoring are ensured; the fault source node is positioned by using the Bayesian network in combination with the power grid topological relation, so that the fault point is quickly and accurately judged, the positioning time is shortened, the monitoring and management level of the power system is improved, and the stability and safety of the system are enhanced.

The invention discloses a capacitive touch screen touch control adjusting method and device and electronic equipment, and relates to the field of display screens. The method comprises the following steps: acquiring environmental parameters of a plurality of touch points of the capacitive touch screen; determining a calibration touch threshold value corresponding to each touch point according to the environmental parameters; obtaining the touch signal intensity of each touch point; the touch signal intensity of each touch point is compared with a corresponding calibration touch threshold value, the touch state of each touch point is obtained, and the touch state comprises an untouched state and a touched state; generating a first compensation touch point based on the touch state of each touch point, and generating a second compensation touch point based on the touch state of each touch point; and based on the touch state of each touch point and the compensation touch point, generating a touch event corresponding to the touch operation, and sending the touch event to the target program to update a user interface corresponding to the target program. By implementing the technical scheme provided by the invention, the accuracy of touch recognition is improved.

The invention relates to the technical field of resource allocation management, in particular to a first-aid medical resource allocation management method and system, a medium and a product, and the method comprises the steps: obtaining call-for-help appeal information, and determining first-aid preparation information based on the call-for-help appeal information, the first-aid preparation information comprising a first-aid route and first-aid vehicle-mounted resources; carrying out patient receiving and transporting based on the first-aid preparation information, and collecting real-time road condition information, patient sign information and patient medical history information in the receiving and transporting process; adjusting a first-aid route based on the real-time road condition information and the physical sign information of the patient, and recording receiving and transporting driving information; real-time hospital rescue resource information is acquired, and on-site medical resources and resource preparation time are determined based on the real-time hospital rescue resource information, the patient sign information and the patient medical history information; on-site preparation information is determined based on the on-site medical resources and the resource preparation time. According to the invention, rationality and accuracy in a first-aid medical resource allocation process can be improved, so that a patient can be ensured to be timely and effectively rescued.

The invention relates to a multi-modal fusion-based fence crossing behavior detection method, which comprises the following steps of: firstly, judging whether a person in a video is overlapped with a fence or not through a semantic segmentation algorithm module so as to judge whether the person is in contact with the fence or not, and then acquiring distance information between the person and the fence by utilizing a monocular depth estimation algorithm module; the abnormal behavior detection algorithm module is used for analyzing whether the behavior of the current person deviates from a normal mode, and finally, the detection results of the three algorithm modules are fused to comprehensively judge whether the person is crossing the fence; according to the method, multiple information sources including semantics, depth, behaviors and the like are utilized, the spanning behaviors can be judged more comprehensively and accurately, meanwhile, the method can be achieved only through a monocular camera, the deployment and maintenance cost is lower, the method is more suitable for complex scenes such as shielding and angles, some tiny abnormal behaviors can be captured, and the method is suitable for popularization and application. All-weather automatic monitoring for efficiently and stably detecting the fence crossing behavior in a complex environment is realized.

The invention discloses a security risk evaluation system for cloud printing data transmission, and relates to the field of cloud printing data transmission security, and the system comprises the following components: a data acquisition module which establishes connection with a server or an interface related to cloud printing data transmission according to configuration information, and performs data acquisition according to a set acquisition frequency; according to the invention, analysis and risk assessment are carried out on a real-time data set through an anomaly detection algorithm and a risk assessment model adopted by the data analysis module, multiple risk factors and indexes are comprehensively considered, quantitative assessment is carried out on abnormal conditions, and the risk assessment accuracy is improved. Therefore, the risk level and possible loss can be judged more accurately, so that the system can discover potential safety risks in time, and the accuracy and efficiency of risk identification are greatly improved.

The invention discloses a thrombolysis prediction method based on deep learning and multi-modal fusion, and belongs to the technical field of thrombolysis prediction, and the method comprises the steps: S1, collecting an MRI image of a patient with ischemic cerebral apoplexy, and constructing a cerebral apoplexy automatic segmentation model; s2, carrying out image registration; s3, lesion segmentation; and S4, thrombolysis prediction. According to the cerebral apoplexy automatic segmentation model based on the gating parallel Mama network, the long-distance dependency relationship in the image can be effectively captured, ischemic cerebral apoplexy lesions can be rapidly and accurately segmented, and the lesion identification efficiency and accuracy are greatly improved; according to the algorithm provided by the invention, the linear time complexity is realized, the computing resource consumption is greatly reduced, and the bottleneck of the prior art is broken through; according to the cerebral apoplexy thrombolysis prediction model based on the multi-modal data, scientific automatic decision support is provided for judging whether the patient is suitable for thrombolysis treatment or not, and the quality and safety of medical decision are improved.

The invention provides an orientation and imaging method and system for sound wave detection data in a tunnel hole, and the method comprises the steps: obtaining sound wave data in different directions responding to the same excitation sound wave, rotating the sound wave data until an orthogonal component disappears, and determining the direction of a reflector according to a rotation angle; carrying out weighted summation on the sound wave data acquired by the receiving array to obtain scalar output, calculating an optimal weight to receive a target signal from a reflector orientation to the greatest extent, and carrying out weighted processing according to the optimal weight to obtain a superposed signal; and solving a three-dimensional elastic wave wave equation based on the processed superposed signal to obtain a forward wave field and a reverse wave field, and performing migration imaging by using a cross-correlation imaging condition to obtain a three-dimensional migration imaging result. The method can enhance the signal-to-noise ratio of the weak reflection signal, and achieves the quick and accurate judgment of the orientation of the target body in front of the tunnel face.