28results about How to "Avoid neglect" patented technology

The invention provides a road tunnel traffic incident real-time prediction method based on multi-model fusion, and the method comprises the steps: S1, collecting traffic data from a plurality of data sources, carrying out the data preprocessing of the traffic data, dividing the processed data into a training set, a verification set and a test set according to a specific proportion, carrying out normalization processing on data of the training set and the test set; s2, designing and training the processed data to obtain M models; and S3, constructing a stack model from the M models obtained in the S2 by adopting a stack ensemble learning strategy, and inputting the prediction probability of each model as feature input into the stack model for prediction to obtain a final prediction result. The problems that the prediction accuracy and reliability are insufficient due to the fact that complex traffic data features are difficult to fully capture in the prior art, the characteristics of various models cannot be effectively fused, the advantages of an integrated model cannot be fully played, and the prediction effect is uneven in hierarchy are solved.

The invention provides an engine vibration data collection and storage method, medium and system, and belongs to the technical field of engine vibration collection, and the method comprises the steps: collecting vibration data to obtain a vibration original data matrix, decomposing the vibration original data matrix to obtain a vibration preprocessing data matrix and vibration unconventional data, respectively carrying out time domain decomposition and wavelet transformation to obtain a time domain feature data matrix and a frequency domain feature data matrix, carrying out feature extraction and dimension reduction to obtain a dimension reduction conventional feature matrix, and carrying out calculation and analysis to obtain a vibration unconventional feature matrix; calculating the feature correlation, feature contribution degree and feature weight of the dimensionality reduction conventional feature matrix and the vibration unconventional feature matrix, performing feature fusion to obtain a vibration comprehensive feature matrix, outputting the vibration comprehensive feature matrix, and performing parameter adaptive adjustment of the sampling rate and the filtering parameter according to the vibration comprehensive feature matrix to obtain the vibration characteristic matrix. The technical problem that an existing method lacks effective acquisition of some instant irregular and sudden abnormal vibration signals and is easily neglected in the acquisition process is solved.

The invention relates to the technical field of agriculture, in particular to an agricultural product disease real-time monitoring and early warning system which comprises an environment monitoring module, a crop growth monitoring module, a disease detection module, a data analysis module and an early warning generation module. Wherein the environment monitoring module is used for monitoring environment parameters of a farmland in real time; the crop growth monitoring module is used for collecting growth state data of crops; the disease detection module is used for detecting disease information of crops in real time; the data analysis module is used for comprehensively analyzing the monitored data, evaluating the current disease risk and predicting the future disease development trend; and the early warning generation module is used for generating corresponding disease early warning information. According to the invention, environment monitoring, crop growth state monitoring and disease detection are fused, accurate disease risk assessment and prediction are carried out in combination with a decision tree algorithm, and real-time and accurate disease early warning is realized, so that farm management personnel are helped to carry out early warning.

The invention discloses a hair care comb control system and method based on hair quality detection, and relates to the technical field of hair quality detection.A personalized application mode of a hair care comb is started according to user hair quality data and preference information, when the user hair quality data is abnormal, multi-level alarm instructions are sent to the outside, and alarm channels are switched; the hair quality management knowledge graph gives improvement measures according to the hair quality data of the user, the hair-care comb product database recommends corresponding products for the user, and recommendation matching of the products and the improvement measures is dynamically adjusted; the method comprises the following steps that: a hair-care comb optimizes improvement measures and then sends the improvement measures to a user, if the hair quality still has specific abnormity, the hair-care comb pushes content to the user and provides online consultation service, and the pushing frequency of paid consultation service is constrained according to a plurality of latest continuous abnormity degrees. The method prevents the user from neglecting the current hair quality problem, updates and switches the alarm channel, and ensures that the user can give feedback when the user neglects the alarm instruction or does not receive the alarm instruction.

The invention, which relates to the technical field of the energy storage equipment, discloses a fire multi-stage early warning system for energy storage equipment, and the system comprises the steps: collecting the operation temperature data of a lithium battery pack, generating an operation temperature value, and starting a fire early warning mechanism if the operation temperature value exceeds an operation temperature threshold value; if the abnormal degree of the fire monitoring data of the potential risk area exceeds the expectation, a first-level alarm instruction is sent out and corresponding feedback data is received, if the feedback data is not consistent with the expectation, a second-level alarm instruction is sent out, the existing fire risk is processed, and after a traversal area is identified according to the fire state data, the traversal area is identified. An emergency escape path is planned in the early warning area, and if the emergency escape path does not exist, a third-level alarm instruction is sent out; and if the secondary alarm instruction is not received, the battery pack temperature adjustment knowledge graph is matched with a corresponding processing strategy. The lithium battery pack is maintained in a targeted manner, the running state of the lithium battery pack is improved, and subsequent potential safety hazards and fire risks are reduced.

The invention discloses a quick retrieval method and device for multi-modal data and a storage medium. The quick retrieval method and device for the multi-modal data are used for quickly retrieving the multi-modal data. The method comprises the following steps: collecting a plurality of public data sets, wherein the data sets comprise data types of at least two different modalities; labeling the data set to generate labeling information related to the multi-modal data; performing feature extraction on the labeling information in the data set by using a multi-modal feature extraction model to generate a multi-modal feature vector; according to the feature vectors, a cross-modal feature fusion algorithm is used, weighted average of all modal feature vectors is calculated based on an attention mechanism, and fusion feature vectors are generated; according to the related multi-modal data, calculating the similarity of the feature vectors by using a weighted cosine similarity algorithm; retrieving related multi-modal data from the fused feature vectors through the similarity of the feature vectors according to the feature vectors input and queried by the user; and sorting the multi-modal data according to the correlation to obtain a retrieval result.

The invention discloses a management and control system for distributed photovoltaic power generation. The management and control system comprises a plurality of combined early warning modules, a plurality of cleaning comparison modules, a data transmission subsystem, a composite secrecy subsystem, a plurality of sensor acquisition units, a cloud platform and a remote terminal, the cloud platform calculates a plurality of environmental parameters by using a BP neural network to obtain a cleaning threshold value and a fixed period; in any cleaning comparison module, a comparison board is used for simulating the same surface pollution condition of a corresponding photovoltaic module, a detection board is used for automatically cleaning in a fixed period, an MCU is used for obtaining a voltage comparison value and receiving the fixed period and a cleaning threshold value from the cloud platform, the detection board is automatically cleaned according to the fixed period, and when the voltage comparison value reaches the cleaning threshold value, the detection board is automatically cleaned according to the fixed period. And the MCU sends feedback information to the cloud platform. According to the invention, the BP neural network and the cleaning contrast module are combined, and the cleaning threshold and the fixed period can be adaptively adjusted based on the environmental parameters, so that when the photovoltaic module needs to be cleaned can be accurately known, and the photovoltaic module can generate power efficiently.

The invention discloses an electric quantity prediction platform management system based on power generation operation analysis, and relates to the technical field of power dispatching, a trained power consumption demand prediction model is used to predict the power consumption state in a power supply area, a power supply prediction data set is generated, and if it is predicted that power supply is abnormal, the power supply is abnormal. A multi-level alarm mechanism is started in the power supply area, and an alarm instruction is sent to the outside; dividing the power supply area into a plurality of sub-areas according to the key factors obtained through identification, and outputting a corresponding response strategy by a response strategy library according to the user group portraits in the sub-areas; and constructing a supply anomaly degree and an anomaly threshold value according to the feedback data after the response strategy is executed, and selecting a corresponding feedback measure for the power supply area according to a relationship between the supply anomaly degree and the anomaly threshold value. When the power demand and supply in the power supply area are abnormal, targeted and personalized response is quickly made, and current power supply imbalance is avoided.

The invention discloses a control system and method for laser Doppler vibration measurement equipment, and relates to the technical field of vibration measurement equipment control, an emergency processing strategy library outputs a corresponding emergency processing strategy, and the equipment is processed after the emergency processing strategy is executed; an operation value is built according to feedback data of the received first-level alarm instruction, if the operation value exceeds the expectation, a second-level alarm instruction is sent to the outside, and key components of the equipment are maintained on time nodes meeting constraint conditions; and carrying out fault diagnosis by using the trained equipment fault diagnosis model, if the equipment has an operation fault, providing a maintenance scheme by an equipment fault maintenance knowledge graph according to fault characteristics, automatically switching key parts generating the fault, correcting measurement data, and outputting an actual measurement value and a measurement correction value. On the basis of controlling the equipment, the existing fault is maintained in a targeted manner according to the maintenance scheme.

The invention relates to the technical field of dam safety, in particular to a reservoir dam leakage monitoring method and system and a medium. Calculating leakage risk weights and comparing the leakage risk weights with a preset threshold value to obtain leakage data acquisition points; real-time leakage abnormal indexes are calculated according to the leakage data of the collection points, comprehensive leakage abnormal indexes are obtained by combining the real-time leakage abnormal indexes of the multiple time nodes, and a leakage abnormal index function is further obtained; if the function value is greater than a preset leakage abnormity index threshold value, dividing the coordinate points corresponding to the function value into leakage risk areas; auxiliary data are obtained, whether the leakage risk area is a leakage area or not is judged, and if the leakage risk area is the leakage area, early warning is carried out; and finally, establishing a visual platform to display data related to the leakage problem of the gravity dam. By designing the leakage risk weight and the comprehensive leakage abnormal index, the real-time monitoring efficiency of the leakage problem of the gravity dam is improved.

The invention relates to the technical field of equipment adjustment, in particular to an intelligent weeding machine automatic height adjustment method based on terrain height perception, which comprises the following steps: acquiring depth data and terrain data of an operation land parcel through a multi-mode perception device; based on the acquired depth data and topographic data, reconstructing a three-dimensional model of the working plot by using a three-dimensional topographic reconstruction algorithm, and extracting regional topographic features; generating an optimal operation path by using a path planning algorithm; dynamically calculating the target height of the weeding execution mechanism; the cutter angle of the weeding mechanism is adjusted through an angle optimization algorithm so that the cutter angle can be attached to the terrain at the bottom of the gully; and executing target height and angle adjustment. Compared with a traditional weeding machine which only depends on a fixed height or a simple ground detection method, the weeding machine has the advantages that fine adjustment can be carried out according to topographic changes, the weeding efficiency and the operation quality are greatly improved, and missing weeding or excessive weeding is avoided.

The invention relates to the technical field of wind driven generator fault prediction, in particular to a wind driven generator fault prediction system based on machine learning, and the system comprises a deviation data detection module which determines the wind facing accuracy of a wind driven generator and outputs a deviation data detection result; the deviation change trend analysis module is used for identifying a deviation change development trend and predicting a future deviation increase condition; the equipment real-time data acquisition and synchronization module is used for acquiring equipment real-time data related to the yaw system; and the equipment and deviation relation analysis module is used for generating an equipment fault judgment result and judging specific equipment or parts causing small-amplitude deviation. According to the method, multi-level equipment data analysis is carried out, specific equipment causing deviation is deeply analyzed, a fault source is accurately positioned, it is ensured that the working state of a yaw system is rapidly adjusted, deviation is prevented from being further aggravated, it is ensured that the wind driven generator always keeps the optimal matching degree with the wind direction, the power generation efficiency is improved, and energy loss caused by deviation is reduced.

The invention discloses a real estate supervision method and system based on artificial intelligence. The method comprises the steps of data acquisition, data preprocessing, real estate data balancing, real estate property right risk assessment model construction and real estate supervision. The invention belongs to the technical field of real estate management, and particularly relates to a real estate supervision method and system based on artificial intelligence. According to an included angle between real estate data, favorable neighbors are screened out to construct a self-adaptive neighborhood set, similarity measurement is introduced to calculate the number of synthetic data, and the data are synthesized; detecting the quality based on the classification confidence of the synthetic data, and constructing a real estate data balance set according to the high-quality synthetic data; a dynamic frequency factor is designed to calculate a frequency adjustment loop learning rate, a dynamic cutting threshold is introduced to carry out gradient norm constraint, depth Q network parameters are updated, a real estate property right risk assessment model is constructed, the real-time requirement is met, and it is ensured that different types of real estates can be equally and effectively supervised.

The present application relates to the technical field of auxiliary identification positioning, in particular to an AI-assisted lesion positioning method and system based on a gastrointestinal endoscope. Based on segmentation boxes of different sizes, a gastric ulcer lesion area and a corresponding confidence level in a gastrointestinal endoscope image are identified. The completeness value of the gastric ulcer lesion area is determined. According to the number of times that a pixel point at each position is divided into the gastric ulcer lesion area under segmentation boxes of different sizes, in combination with the confidence level and the completeness value of the gastric ulcer lesion area to which the pixel point is divided, the region belonging value of each pixel point is obtained. Based on the region belonging value, the pixel points are merged to obtain a preliminary screening lesion area. The region belonging value of the pixel points and the distribution of the region in the preliminary screening lesion area are analyzed to determine the performance bias degree of each preliminary screening lesion area. Based on the performance bias degree, a target gastric ulcer area is determined from the preliminary screening lesion area. The present application improves the identification accuracy of the target gastric ulcer area.

The invention discloses an electro-optic paint for a bicycle and a spraying process thereof, and particularly relates to the technical field of paints.The electro-optic paint comprises a backboard paint, a medium paint, a luminous paint and a transparent conductive paint which are sprayed in four layers. By controlling the construction conditions, the paint film is uniform and proper in thickness, so that the characteristics of uniform luminescence of the paint surface and bright lighting effect are achieved. Autonomous light emitting is achieved through the coating, a better warning effect can be provided for a vehicle body compared with a reflector or a flashing light, the weight of the vehicle body is effectively reduced under the same effect, and the vehicle is safer and more environmentally friendly.

The embodiment of the invention provides a message pushing method and system based on burying point data and related equipment, belongs to the technical field of computers, and is suitable for the field of financial science and technology. The method comprises the following steps: firstly, in response to an operation prompt request, obtaining a current buried point website, operator information and target buried point data, obtaining current configuration information of the current buried point website, and further screening out reference configuration information from preset historical configuration information based on the current configuration information; and finally, generating an operation prompt message according to the reference configuration information and the target burying point data, and determining a client according to the operator information, thereby prompting the operation prompt message through the client. Therefore, according to the embodiment of the invention, the operation prompt message can be efficiently and accurately generated, and the operation prompt message can be accurately and timely transmitted to the operation personnel, so that the operation personnel can effectively receive and understand the operation prompt message.

The invention discloses a learning plan recommendation method, system and educational robot, which relates to the technical field of educational robots. The method uses a trained personalized learning demand identification model to identify the demand, outputs the identified personalized learning demand; matches learning resources and learning plans according to the personalized learning needs of target students, and outputs the optimal learning path; collects feedback data for comprehensive evaluation and generates a state coefficient from the evaluation data; if the state coefficient does not exceed the expectation, uses a multi-objective optimization algorithm to optimize the personalized optimization learning path of the target student, and classifies the target students based on a pre-trained density clustering algorithm; after identifying abnormal learning behavior, a multi-level alarm mechanism is enabled, and according to the abnormal learning behavior, the corresponding response strategy is output from the emergency response strategy library. The current learning path is adjusted and optimized to make the student's learning path more matched with the current learning resources and learning plan.

This utility model discloses a construction equipment for building composite insulation boards, belonging to the technical field of construction equipment. The equipment includes a trolley frame with a screw rotatably connected to it. A slide rod is fixedly connected to the trolley frame at the side of the screw. A first positioning frame is movably mounted on the screw and slide rod, and a second positioning frame is rotatably connected to the side of the first positioning frame. The equipment uses a stepper motor to drive the screw to rotate, making it easy to change the height of the first and second positioning frames and adjust the included angle between them. This facilitates the clamping and fixing of the insulation board at the corner of the exterior wall, improving practicality. Furthermore, the suction cups on both sides of the clamping board adhere to and fix the insulation board, resulting in better fixation. When the insulation board slides downwards, it separates from the suction cups, making it easy to detect whether the insulation board has moved, preventing workers from overlooking it and allowing for timely adjustment of the insulation board's position.

Provided are an information processing apparatus, an information processing method, and a program capable of improving visibility of a detection target in an image and realizing display that can prevent overlooking. The information processing apparatus includes: a processor, in which the processor acquires an image, executes processing of region extraction to extract a region of a detection target from the image, generates position information of the region from region information of the extracted region, and displays a result of the processing of the region extraction by switching between a first display aspect in which information for informing of a position of the region is displayed on a display screen based on the position information in a visually appealing manner and a second display aspect in which the region information is displayed in an aspect different from the first display aspect according to at least one of a size of the extracted region or a display size of the region displayed on the display screen.

This application provides an adaptive integrated land surface classification method and system based on multi-scale spatiotemporal partitioning, belonging to the field of remote sensing image processing technology. The method includes: acquiring multi-source remote sensing image data, digital elevation model (DEM) data, and historical land use data; partitioning the target area into spatial units based on the DEM data; identifying the phenological change process of each spatial unit using time-series data; determining spatiotemporal partitions based on the change process, historical land use data, and each spatial unit; constructing a feature set based on the spectral, textural, and index features of the topography and phenological characteristics of each spatiotemporal partition; selecting and applying a corresponding pre-trained land surface classification model based on the feature set and spatial units to obtain the classification result; and establishing a feedback update mechanism based on the classification result, multi-source remote sensing image data, and DEM data. This application improves classification efficiency and accuracy.

The invention discloses an infrared weak and small target detection method and device based on asymmetric penalty sparseness. Comprising the following steps: 1) constructing a to-be-detected image sequence into a three-dimensional space-time tensor # imgabs0 #; 2) carrying out principal component analysis on the # imgabs 1 # to obtain a principal component tensor # imgabs 2 #; 3) constructing an asymmetric penalty sparseness estimation function # imgabs3 # of the target; (4) constructing a tensor nuclear norm # imgabs4 # based on a Laplace operator; 5) establishing an infrared weak and small target detection model based on asymmetric penalty sparseness; the invention discloses an infrared weak and small target detection method based on asymmetric penalty sparseness, and the method comprises the steps of (1) establishing a model in the step (2), (3) establishing the model in the step (3), (4) utilizing an ADMM-based algorithm to solve the model established in the step (5) to obtain a target detection result tensor # imgabs5 #, and (7) converting the # imgabs6 # into a target detection result sequence to achieve infrared weak and small target detection.According to the infrared weak and small target detection method based on asymmetric penalty sparseness, the comprehensive detection performance of infrared weak and small targets can be effectively improved.

The invention discloses an equipment control method and system based on deep learning, and relates to the technical field of compressor control, and the method comprises the steps: obtaining a historical normal sequence and a current operation sequence, and carrying out the preprocessing to obtain a historical feature data set and a current feature data set; calculating the attention weight of each feature under each time scale based on the historical feature data set; dividing the historical feature data set and the current feature data set to obtain a historical fusion window and a current fusion window under each time scale; combining the attention weight to calculate a fusion value and construct a fusion sample, and obtaining a historical fusion sample set and a current fusion sample under each time scale; constructing and training a prediction model to obtain an effective prediction model under each time scale; and calculating a comprehensive valve opening value at the next sampling moment based on the current fusion sample and the effective prediction model. According to the method, the valve opening of the compressor can be accurately predicted, and the high-precision control requirement of the compressor under the complex working condition is met.

This utility model provides a roller surface cleaning mechanism for an electronic belt scale. It relates to the field of electronic belt scale cleaning technology. The roller surface cleaning mechanism includes a base, on which four mounting brackets are fixedly installed. Each of the four mounting brackets is equipped with a connecting component. Each connecting component includes: two upright blocks, a connecting rod, a circular roller, two upright blocks, two rotating blocks, two connecting rods, two rotating blocks, two circular rollers, and two upright blocks. Two upright blocks are fixedly installed on the top of their respective mounting brackets. The connecting rod is fixedly installed between the two upright blocks. The circular roller is rotatably mounted on the connecting rod. The two upright blocks are fixedly installed on the top of their respective mounting brackets. This utility model has the advantages of accelerating cleaning efficiency, reducing time and effort spent, and relatively improving cleaning efficiency.

The embodiment of the invention provides an abdominal circumference monitoring belt, system and method for prognosis evaluation of a liver cirrhosis patient, and relates to the field of medical instruments. The abdominal circumference monitoring band for prognosis evaluation of the liver cirrhosis patient comprises a base, two elastic bands, a connecting assembly and a monitor, the monitor is connected with the base, the two elastic belts are fixedly connected with the base, and the ends, away from the monitor, of the two elastic belts are detachably connected with the connecting assembly. The monitor comprises two monitoring assemblies and two monitoring belt bodies. The two monitoring belt bodies are wound around the two monitoring assemblies respectively, and the ends, away from the monitoring assemblies, of the two monitoring belts are connected with the elastic belt. The abdominal circumference monitoring band for prognosis evaluation of the liver cirrhosis patient solves the problem that in the prior art, ascites changes cannot be monitored in real time.

The invention relates to a low-resistivity oil reservoir identification method, and belongs to the field of low-resistivity oil reservoir identification, and the low-resistivity oil reservoir identification method comprises the steps: obtaining the porosity, resistivity and historical oil test and production test data in historical drilling data, building a corresponding physical property-electrical property-oil content relation chart for each sedimentary microfacies by using the data, and obtaining the corresponding physical property-electrical property-oil content relation chart for each sedimentary microfacies; acquiring a porosity lower limit and a resistivity lower limit corresponding to the oil reservoir under each sedimentary microfacies through the drawing board, and taking the porosity lower limit and the resistivity lower limit as standards for determining whether the corresponding sedimentary microfacies reservoir contains oil or not; different identification standards are set for different sedimentary microfacies reservoirs, so that the identification accuracy of the low-resistivity oil reservoir is improved.

The invention discloses a rocket artificial rainfall enhancement influence area estimation method and system. The method comprises the steps of A1, collecting rocket rainfall enhancement operation parameter data, regional wind field data and atmospheric water vapor distribution data, and performing preprocessing respectively; a2, respectively extracting structured diffusion characteristics, regional wind field characteristics and water vapor distribution characteristics; a3, carrying out cross-modal fusion to obtain a multi-modal fusion feature and a catalyst diffusion feature; a4, extracting catalyst-water vapor interaction characteristics; a5, calculating a basic influence area and a basic grid range image of the rocket artificial precipitation enhancement operation; and A6, performing grid statistics and proportional scale conversion on the accurate influence area of the artificial precipitation enhancement operation of the rocket to obtain the accurate influence area of the artificial precipitation enhancement operation of the rocket. According to the method, the problem that the estimation accuracy of the rocket artificial precipitation influence area is insufficient due to the fact that linear source diffusion is simplified into a trapezoidal model and multiple factors are not fully considered in a traditional method can be solved.

The application discloses a human-computer collaborative intelligent interaction state tracking method and system, relates to the technical field of human-computer collaborative interaction, and comprises the following steps: acquiring first feature data and second feature data; extracting amplitude information of the first feature data, and generating a first identifier and a first parameter according to whether the amplitude information is lower than a first threshold value; in the case that the first identifier is captured, third feature data of the second feature data is extracted; based on a second identifier of a terminal, the first parameter and the third feature data, target score information is determined; in the case that the target score information satisfies a second threshold value, a target signal is issued to an external collaborative device, and an asynchronous state backtracking check is triggered; the application avoids the emergency interaction instruction of the cloud traditional Chinese medicine platform being ignored in an extreme care scene.

The invention provides a master-slave database configuration detection method and device, a storage medium and computer equipment. The method comprises the following steps: acquiring a context configuration file of a changed application code, each initial function method and corresponding class information; obtaining each objective function method for database interaction through the context configuration file, and obtaining each initial Asgard SDK; according to the class information of each target function method, determining a target Asgard SDK (Software Development Kit) of each target function method for database transaction operation; determining each initial call chain according to the call relationship of each target function method, and filtering to obtain each target call chain for the read-only database; and establishing a mapping relationship between a top layer interface of each target call chain and each Asgard SDK, and detecting read-write configuration of the master and slave databases according to each mapping relationship. In this way, the read-write configuration problem of the master database and the slave database is accurately detected.