233results about How to "Confidence" patented technology

The invention discloses an AI auxiliary limb rehabilitation system, and belongs to the field of communication control systems, the AI auxiliary limb rehabilitation system combines a sensing module, an AI analysis module, a VR interaction module, a feedback module and the like, comprehensively collects limb movement and physiological data, introduces gamification elements, improves the patient participation degree through target setting, an award mechanism and interesting tasks, and improves the rehabilitation effect. A personalized rehabilitation plan is generated by using a deep learning algorithm, an emotion recognition unit can adjust the plan according to a psychological state, environment self-adaption and real-time adjustment of training difficulty to ensure training safety and is challenging, data cloud storage facilitates remote monitoring, third-party equipment is compatible with extended monitoring ability, and the training efficiency is improved. A brand-new, efficient and personalized solution system scheme is provided for limb rehabilitation.

The invention belongs to the technical field of three-dimensional simulation, and particularly provides a rock mass structural plane network simulation method fusing artificial intelligence and laser scanning, and the method comprises the steps: obtaining point cloud data; clustering the point cloud data by adopting a clustering algorithm to obtain an original structural surface; performing statistical analysis on the original structural plane according to the rock mass type and the structural partition to obtain a representative structural plane, and fitting to obtain a probability density function according to occurrence parameters of the representative structural plane; according to the development volume density of the representative structural plane, converting the probability density function into a volume density function, and according to the volume density function, generating a random structural plane by using a Monte Carlo simulation method; and constructing a BIM model of the rock mass structure, and performing three-dimensional space simulation on the rock mass structural plane network based on the BIM model and the random structural plane to obtain a simulation result. According to the method, the precision, comprehensiveness and authenticity of depicting the rock mass structural plane are greatly improved, and the spatial distribution characteristics of the structural plane network can be quantitatively evaluated.

The invention discloses a hidden disaster-causing factor disaster-causing risk evaluation method based on multi-source data fusion, and relates to the technical field of geographic information systems. The method comprises the following steps of S01, data acquisition, S02, data preprocessing, S03, feature extraction, S04, risk assessment model construction, S05, model training and optimization, S06, risk assessment, S07, result analysis and verification and S08, result output. According to the scheme, by adopting an advanced multi-source data fusion technology, various data sources such as geological data, remote sensing images, elevation maps and hydrological data are effectively integrated; the integration not only widens the coverage range of the data, but also improves the precision and timeliness of the data. Through an automatic data preprocessing process, it is ensured that data of different sources and types can be effectively fused, and a data consistency relation is constructed; the data integration strategy can more comprehensively reflect the complexity and dynamic change of hidden disaster-causing factors, so that the accuracy of disaster-causing risk assessment is remarkably improved.

The invention belongs to the field of track foreign matter detection, and discloses a track foreign matter detection method based on a YOLO algorithm. Comprising the following steps: collecting track foreign matter images to form a data set, and performing sample expansion processing and label processing on each image in the data set so as to obtain a sample set; a channel attention mechanism and a direction perception mechanism are added into the YOLOv5, constraint conditions are set, and then an improved YOLOv5 algorithm is obtained and marked as a G-YOLOv5 model; a G-YOLOv5 model is pre-trained by using the sample set, and a trained G-YOLOv5 model is obtained; a real-time image of the track is collected and processed, the processed image is imported into the trained G-YOLOv5 model, and a real-time detection report is obtained; the obtained real-time detection report is arranged on a visual interface, and efficient track foreign matter detection based on the YOLO algorithm is achieved.

The invention discloses a wind power prediction method, system and equipment based on multi-model fusion, relates to the technical field of wind power prediction, and realizes non-parametric probability prediction of short-term wind power prediction by combining a Pearson's correlation coefficient, a frost optimization algorithm optimization variational mode decomposition model, a CNN-ASSA-Informer model and quantile regression. Complex time series data are processed through an optimization algorithm, and the complex relation between different frequency components and wind power in the time series data is solved. And a corresponding prediction interval is constructed, so that the precision and confidence of wind power prediction are improved, and a more reliable wind power prediction result can be provided for regional wind power scheduling, renewable energy integration and an electric power market.

The invention provides a fan fault diagnosis method and system, and the method comprises the steps: installing a sensor at a key part, collecting an acceleration signal, and carrying out the filtering processing; performing time domain and frequency domain analysis, and extracting a root-mean-square value, an amplitude entropy value and an instantaneous frequency value; establishing a model based on the instantaneous frequency to calculate a vibration complexity index; a fault diagnosis model is established in combination with the indexes to analyze the component fault probability; setting a risk threshold, comparing the analysis result and generating a diagnosis conclusion; according to the invention, the accuracy and the real-time monitoring capability of fan fault diagnosis can be improved, the maintenance cost is reduced, the reliability and the intelligent level of equipment are improved, and the user experience is enhanced.

The invention discloses a charger communication test method and system, a storage medium and a program product, and the method comprises the steps: obtaining a state parameter of a to-be-tested charger; collecting a communication message of the charger to be tested, and constructing a message time sequence queue; generating a message interval sequence reflecting the time sequence characteristics of the message; calculating a discrete degree coefficient of a message time sequence according to the message interval sequence; recording record data at an abnormal moment when the communication abnormality occurs, and inputting the record data into a preset state transition matrix to obtain a corresponding relationship; calculating probability distribution of communication abnormity under different state parameters to obtain a correlation model; generating a message sequence for testing based on the association model, wherein the time sequence characteristic of the message sequence for testing is the same as the state parameter and the dispersion degree coefficient corresponding to the high-probability abnormal scene of which the probability is greater than a preset probability threshold in the association model; and sending the test message sequence to the to-be-tested charger to detect response data of the to-be-tested charger. And the accuracy and efficiency of the communication test of the charger are improved.

The invention provides a retrieval method, system and equipment based on webpage search and a knowledge graph and a medium, and belongs to the technical field of intelligent retrieval. The method comprises the following steps: acquiring an input problem, and performing problem analysis and field classification by utilizing a large language model to generate field features; the method comprises the following steps: retrieving related documents from the Internet according to an input problem by using multi-stage retrieval of webpage search and combining sparse retrieval and dense retrieval technologies, and constructing a candidate document pool as a first retrieval result; according to an input question, performing structured query of the knowledge graph, and extracting entity and relation information related to the question as a second retrieval result; according to the domain features, calculating a corresponding information change rate, and according to the information change rate, carrying out adaptive integration on the first retrieval result and the second retrieval result to generate a target answer; and evaluating the confidence coefficient of the target answer by using a large language model, verifying the target answer according to the confidence coefficient, and determining output content based on a verification result.

The invention provides a method for identifying chromosome translocation by integrating three-dimensional genome and three-generation genome data, and relates to the technical field of biology.The method comprises the steps that three-generation genome sequencing data of a to-be-detected sample is obtained, and chromosome translocation is identified based on the three-generation genome sequencing data to form a first data set; obtaining Hi-C sequencing data of a sample to be detected or sequencing data based on a Hi-C derivation technology, and identifying chromosome translocation based on the Hi-C sequencing data or the sequencing data based on the Hi-C derivation technology to form a second data set; and taking candidate chromosome translocation in the intersection of the first data set and the second data set, and taking the verified candidate chromosome translocation as the finally screened chromosome translocation. The translocation accuracy of the chromosomes screened by the method is high and can reach 98%.

The invention relates to an underwater acoustic target recognition method based on knowledge injection deep learning, and the method comprises the following steps: S1, collecting target echo data through employing an active sonar, or collecting target radiation noise data through employing a passive sonar, and carrying out the multi-stage labeling of a target according to an actual demand, and obtaining a multi-stage label tree; s2, constructing an underwater acoustic target recognition deep learning network model based on knowledge injection, wherein the model comprises two parts: a feature extraction module and a knowledge injection module based on multistage labels; according to the method, the neural network learns the reasoning process of a person, the confidence of sonar target recognition is improved through layer-by-layer judgment while the deep learning nonlinear feature representation capability is fully utilized, and the explanatability of deep learning model output is also improved through multi-level labels.

The invention discloses a file data circulation security system based on a block chain technology, and the system deploys block chain nodes based on the block chain technology, and constructs a block chain network system. An upper-layer application is established on the basis of digital identity authentication, a security policy smart contract, process management and control and uplink log auditing, and the upper-layer application comprises a block chain background management module, a file storage cloud service module and a component core capability module; in a business application level, a data owning party and a data using party can realize safe circulation of data through encryption authorization and authorization decryption services provided by the platform. According to the method, data privacy can be protected, data traceability and non-tampering performance are achieved, and meanwhile efficient data circulation and cross-platform interoperability can be achieved.

The invention relates to the technical field of ultrasonic thickness measurement, in particular to a sewage pipeline scale deposition thickness measurement method and system. The method comprises the following steps: firstly, obtaining all similar ultrasonic wave band groups among all ultrasonic wave signals at the same measuring point, and further screening out all stable wave band groups; symmetrically fusing the two ultrasonic signals of each group of preset measuring points to obtain confidence weights of fused ultrasonic signals of each group of preset measuring points under each frequency; and finally, evaluating the scale deposition thickness of the sewage pipeline based on all the fused ultrasonic signals and the corresponding confidence weights. According to the method, the stable ultrasonic wave section is determined through the similar fluctuation characteristics of the ultrasonic wave signals at the same angle and different frequencies, then the ultrasonic wave signals at the measuring points at the symmetrical angle are fused, the confidence coefficient is evaluated, and therefore the pipeline accumulated dirt thickness is accurately evaluated based on the confidence coefficient and the fused ultrasonic wave signals; the interference of other external factors on the ultrasonic signals is reduced, and the measurement accuracy of the pipeline scale deposition thickness is improved.

The invention relates to the technical field of electronic tracking marks, in particular to a whole-course traceable pig intelligent electronic ear tag and method for livestock breeding. The live pig intelligent electronic ear tag comprises a nonlinear characteristic analysis module, a behavior trend modeling module, a coding strategy adjustment module, a compression efficiency evaluation module, an abnormal mode monitoring module, a resource adjustment strategy module and a behavior early warning feedback module. And the nonlinear characteristic analysis module processes time sequence data by adopting a chaos theory analysis method based on live pig monitoring data. According to the invention, by integrating nonlinear characteristic analysis, behavior trend modeling, coding strategy adjustment, compression efficiency evaluation, abnormal mode monitoring, resource adjustment strategy and behavior early warning feedback modules, the efficiency of livestock breeding management is improved. The application of a chaos theory analysis method and a dynamic modeling technology makes prediction of live pig behaviors and identification of abnormal modes more accurate.

The invention relates to the technical field of risk early warning, and particularly discloses a risk early warning interaction method and system for intelligent ankle pump training detection and analysis. The method comprises the following steps of S1, establishing an ankle pump trainer database, S2, collecting ankle pump training data of a trainer, S3, analyzing physiological index data, S4, evaluating physiological index abnormity, S5, analyzing ankle joint activity data, S6, evaluating ankle joint injury risk, and S7, evaluating ankle joint injury risk. Step S8, establishing a risk early warning index model and carrying out risk assessment; and step S8, carrying out risk early warning interaction. By analyzing the physiological index abnormity evaluation coefficient, the ankle joint injury risk coefficient and the ankle pump training risk early warning index, risk grade division is carried out, early warning interaction information display is carried out on risks of different grades, and through real-time physiological index feedback and injury risk evaluation, a trainee is helped to know the training state of the trainee in time.

The invention discloses a power failure information monitoring method and system based on a real-time position, and relates to the technical field of power failure information monitoring, and the method comprises the steps: collecting the data of an intelligent electric meter, calculating the power flow of a line, carrying out the sparse regression through an LASSO algorithm, and recognizing a key line which is strongly related to a power failure event; window sliding processing is carried out based on the key line, and an event matrix is generated according to statistical characteristics; the TF-IDF value of each feature is calculated based on the time frequency of the statistical features in the event matrix. According to the method, power flow data and voltage phase angle data are used as feature matrixes, the sparsity of LASSO regression is beneficial to eliminating non-critical line data with large noise or interference, the complexity and redundancy of a model are reduced, feature importance of a critical line in different time windows is enhanced through a TF-IDF weighting matrix, and the accuracy of the LASSO regression is improved. According to the method, the SVM model is helped to recognize the power failure event more accurately, and after SVM preliminary classification, isolated misjudgment can be corrected through MDC auxiliary recognition and clustering analysis.

The invention discloses an interpretable knowledge graph error detection and repair method, system and device, a medium and a product, and relates to the field of knowledge graph detection. Each triple comprises a head entity, a relation and a tail entity; the method comprises the following steps: determining the confidence coefficient of each triple in the knowledge graph based on a link mode of the knowledge graph; the link mode comprises a shared head entity and a shared tail entity; dividing the triads in the knowledge graph into high-quality triads and error triads according to the confidence coefficient of each triad and the set confidence coefficient; determining a plurality of high-quality logic rules based on the high-quality triad; and repairing the error triad based on the plurality of high-quality logic rules. According to the method, errors in the knowledge graph can be detected, and the errors are repaired and explained through a high-quality logic rule. The quality of the knowledge graph data is improved.

The invention provides an abnormal annotation content repairing method and device and electronic equipment, and the method comprises the steps: obtaining the original annotation content of an original image and the predicted annotation content outputted by the prediction of the original image through a target model, and carrying out the superposition processing of the original annotation content and the predicted annotation content, and obtaining the mixed annotation content, the original annotation content comprises an original annotation box and a confidence coefficient thereof, the predicted annotation content comprises a predicted annotation box and a confidence coefficient thereof, and the original annotation content is abnormal; aiming at the plurality of coincidence degree evaluation indexes, determining a coincidence degree threshold value corresponding to each coincidence degree evaluation index; taking any two labeling boxes in all the labeling boxes of the mixed labeling content as a labeling box combination, and deleting the labeling boxes with relatively low confidence in any labeling box combination under the condition that any labeling box combination meets at least one overlap ratio threshold, and determining the deleted mixed annotation content as a repair result of the original annotation content.

The invention provides a display device and a data processing method of a distance sensor, the display device comprises a display device, a human body detection device and a control device, the human body detection device comprises a temperature sensor and a distance sensor, the control device is specifically configured to determine whether a human body exists on the display side of the display device according to object position data from the distance sensor and temperature data from the temperature sensor; according to the embodiment of the invention, the object position data is managed through the linked list, the linked list is used for storing the object position data of the target object, and the object position data from the distance sensor is stored and managed through the linked list, so that the management efficiency of the data of the distance sensor is improved through the linked list.

The invention relates to the technical field of English teaching, in particular to an intelligent English teaching system for English teaching, which comprises a data storage server, a student terminal, a data exchange server and a teacher terminal, the student terminal comprises an input unit, a data acquisition unit, a planning unit, an auxiliary unit, a processor, an execution unit, a recording unit, a rechecking unit and a management unit; the input unit is used for receiving and recording target data set by a user, and the target data comprises a target location and preset time; the intelligent English teaching system provides simulation questions in recent three years and an intelligent recheck mechanism by precisely customizing a learning plan, and effectively improves the pertinence and efficiency of English learning. The system can automatically adjust learning content and strengthen weak links according to requirements of students, ensures that the students comprehensively master English knowledge, provides powerful support for English examination, and remarkably improves academic records and examination taking ability of the students.

The invention relates to the technical field of transaction optimization, in particular to an energy transaction optimization management method based on a privacy protection block chain, and the method comprises the following steps: collecting data of energy transaction participants and transaction records, encrypting the transaction records through employing a digital signature, checking the safety of energy transaction information, and generating an encrypted transaction data set. According to the invention, the delay of information transmission is reduced through the operation of capturing and updating the block chain account book in real time, so that the market response is more agile, the method is very important for the rapidly changing energy market, and the classification and risk assessment of transactions not only help to identify abnormal transactions, but also further enhance the ability to prevent potential fraud activities, thereby improving the safety of the system. By optimizing the transaction verification rule and the execution path, the energy transaction processing efficiency is improved, the energy distribution is optimized, the resource allocation is more reasonable, and the market competitiveness and sustainability are improved.

The invention provides a farmland road automatic segmentation method, system and device and a medium, and belongs to the technical field of image segmentation, the scheme uses an open source data set to carry out model training and prompt project input, reduces the demand for a special marking sample which is high in cost and difficult to obtain, and especially in a changeful farmland environment, greatly improves the segmentation efficiency of the farmland road. The dependence on the marked data is effectively reduced; meanwhile, by introducing an advanced road segmentation model based on an SAM network architecture and combining with OSM road vector data based on an open source, a prompt project is adaptively generated, so that SAM is activated to extract the farmland road, and the farmland road extraction precision is effectively improved.

The invention discloses a wide-area scene optimal focal length optimizing method based on stepless zooming, relates to the technical field of machine vision and target tracking, and aims to change the focal length by using a pan-tilt camera and adaptively adjusting the size of a target in a picture, so that mounting equipment can be effectively utilized. By means of the stepless zooming optimization technology, the focal length can be dynamically adjusted according to the real-time relative distance between the target and the camera and the change of the field angle, and therefore it is ensured that the size of the target in the picture is matched with the optimal confidence degree of the tracking model. And the definition of the image and the high confidence coefficient of target tracking are always ensured no matter how far the target distance or the size changes. The method does not need to manually adjust the focal length of the lens, does not need to deploy additional equipment, and is suitable for various visual detection scenes under unmanned aerial vehicles and long-focus monitoring view angles.

The invention discloses a confocal laser microscope detection method for micro-plastic surface biofilm components and biomass, and belongs to the field of micro-plastic pollution monitoring. The method comprises the following steps: carrying out stratified sampling and polymer component identification on micro-plastic in an environmental water body; the method comprises the following steps: marking algae, blue-green algae, bacteria and extracellular polymeric substances in a biological membrane through specific fluorescent staining, carrying out high-resolution three-dimensional imaging by utilizing a confocal laser microscope, and realizing batch quantitative analysis of each component of the biological membrane by adopting ImageJ software. The technical problems that an existing detection method is insufficient in parameter dimension, evaluation result deviation is caused only based on partial component quantification, and the influence of the overall structure of the biological membrane on the micro-plastic migration behavior cannot be truly reflected are solved. According to the method, the biomass and spatial distribution of each component of the biological membrane can be comprehensively and accurately quantified, the accuracy of analyzing the vertical migration mechanism of the micro-plastic is improved, and multi-dimensional data support is provided for ecological risk assessment.

The invention discloses a deep foundation pit construction safety risk pre-control method and system based on a control theory and a dual-system theory, and the method comprises the steps: carrying out the risk recognition and risk monitoring of deep foundation pit construction safety based on the expansion of a sensor, determining a risk point, carrying out the pre-control discrimination and pre-control rating of the recognized and monitored deep foundation pit construction safety risk, and carrying out the early warning of the deep foundation pit construction safety risk. And finally, performing pre-control decision making and pre-control execution on the pre-control risk in the step 2 based on an actuator and DST expansion. According to the method, the source risk and the state risk are systematically identified and monitored, potential risks can be timely found and prevented, the operation stability of a project is improved, potential safety hazards can be timely identified and coped with by monitoring various state risks, and the risk risk is effectively prevented. Safety of constructors and the surrounding environment is ensured, and a positive effect on cost control of a project is achieved.

The invention discloses an unmanned aerial vehicle actuator dynamic characteristic test and modeling method based on a real installation state, belongs to the technical field of unmanned aerial vehicles, and comprises a test system constructed based on an unmanned aerial vehicle actuator and a control surface in an in-situ installation state. The test system comprises an MEMS inertial navigation device which is fixed on the surface of the control surface and is closest to a control surface rotating shaft, and the MEMS inertial navigation device is connected with a test computer and a test power supply through cables respectively; a suction cup located on the surface of the control surface is installed at the position L away from the MEMS inertial navigation device, the suction cup is connected with a spring through a rope, and the other end of the spring is connected with a fixed frame; and when the control surface deflects by 0 degree, the rope at one end of the spring is just in a straightened state. According to the dynamic characteristic testing and modeling method for the unmanned aerial vehicle actuator based on the real installation state, the modeling precision of the actuator can be improved, and the reliability of flight control law design and the confidence coefficient of a flight simulation result are improved.

The invention discloses a sliding bearing impact stiffness dynamical model building method, which comprises the following steps of building a lubricating medium flow field finite element model, and obtaining a relationship between the pressure and thickness change of a lubricating medium based on the lubricating medium flow field finite element model; converting the relationship between the pressure and the thickness change of the lubricating medium into the relationship between stress and strain, and fitting by adopting a nonlinear constitutive relationship; processing the bearing bush into a sample, and measuring an impact stress-strain curve of the bearing bush sample under different strain rates; fitting the impact stress-strain curve to obtain a nonlinear constitutive relation of the bearing bush material; and establishing a propulsion shafting simulation model, and endowing a nonlinear constitutive relation of a bearing bush material in material parameters of the propulsion shafting simulation model. According to the sliding bearing impact rigidity dynamic model establishment method, the confidence coefficient of the sliding bearing impact dynamic simulation model is improved, and the impact resistance simulation calculation precision of a propulsion shafting is improved.

The invention discloses an intelligent control method and system for cantilever type tunneling. The invention relates to the technical field of the full-section tunneling process. The coding layer receives an input sequence S, calculates forward and backward candidate hidden states through multiple groups of forward and backward GRU (gated loop) units, forms a hidden state sequence H, inputs the hidden state sequence H into the fuzzy processing layer for fuzzy operation processing, and generates a corrected hidden state sequence H'into the decoding layer; according to the method, various tunneling parameters such as total thrust, cutterhead power and torque are comprehensively considered, a rich input sequence is formed through heat coding operation, comprehensive information is provided for the model, and therefore the accuracy of tunneling speed prediction is improved. The bidirectional Seq-Bi-GRU neural network can capture time sequence dependence and long-term memory effect in data, and compared with a traditional BPNN neural network, the method has higher generalization ability and prediction precision when processing complex sequence data.

The invention provides a Pixhawk flight control-based cluster aircraft semi-physical simulation system. The Pixhawk flight control-based cluster aircraft semi-physical simulation system comprises an airborne computer, a Pixhawk flight controller, an unmanned aerial vehicle networking communication module, a simulation machine and an AirSim simulation environment. The airborne computer is used for collecting and processing sensor data on the aircraft; the unmanned aerial vehicle networking communication module is used for distributed wireless signal transmission; the Pixhawk flight controller is used for autonomously executing a pre-planned flight task through a built-in flight control algorithm; and the simulator and the AirSim simulation environment are used for providing an accurate semi-physical simulation environment and verifying the flight effect through visual simulation. According to the semi-physical simulation system, a real physical model and a simulation environment can be combined, and the confidence coefficient of the system is improved by reducing errors; the operation condition of the distributed cluster aircraft is simulated through collaborative networking, and the research and development efficiency of the distributed cluster aircraft control system is improved.

The application relates to the technical field of battery safety, and discloses a battery safety early warning method and system, an electric vehicle and a computer readable storage medium, wherein the battery safety early warning method comprises the following steps: when a battery is in a constant-voltage charging phase, voltage signals of each cell of the battery are acquired according to a preset time interval; based on the voltage signals of each cell, a voltage change rate sequence corresponding to each cell is calculated; if the voltage change rate sequence of any cell meets a preset abnormality judgment condition, the cell meeting the preset abnormality judgment condition is marked as a risk cell, and a voltage attenuation rate of the risk cell is collected; and in response to the voltage attenuation rate exceeding a preset threshold range of a reference discharge rate, a high-risk alarm is generated. Therefore, hidden failure signs are mined from voltage evolution trends before the battery enters an obvious overvoltage state, and high-confidence active safety early warning is realized.

This invention designs a multi-scale star surface impact crater detection method, system, and medium. The method includes the following steps: S1: Acquire star surface images and segment them into image patches of different scales; S2: Construct multiple impact crater detection sub-models; S3: Train each sub-model using star surface image patches of different scales; S4: Input the segmented star surface image to be detected into each pre-trained sub-model to obtain the detection results; S5: Select the bounding boxes with high confidence and large area among all candidate bounding boxes of each impact crater as the final detection result for that impact crater. This invention can simultaneously identify impact craters of different scales in star surface images with high accuracy, especially improving the accuracy and efficiency of identifying small impact craters, providing more accurate and comprehensive data support for lunar science research, and promoting the in-depth development of lunar exploration and geological research.