72results about How to "Timely identification" patented technology

The invention discloses a crop disease remote sensing monitoring and early warning system based on time sequence spectral information analysis, and relates to the technical field of agricultural disease and insect pest monitoring. Computer drawing software is utilized to divide a corn planting field in equal proportion, and sensors are deployed to monitor the growth environment change in each grid in real time and generate microenvironment data information; the method comprises the following steps: calculating a microenvironment difference index Xbd according to microenvironment data, identifying a microenvironment difference aggregation area and sending a risk analysis instruction, after receiving the risk analysis instruction, capturing related spectral data information of the microenvironment difference aggregation area by using an unmanned aerial vehicle and a multispectral sensor, and constructing a multispectral fluctuation coefficient Xgp by analyzing related spectral data, and comparing the calculated rust disease risk assessment index Zpg with a preset threshold W in combination with the microenvironment difference index Xbd and the trained risk assessment model, and obtaining corresponding grade early warning according to the rust disease risk degree.

The invention discloses a water environment safety early warning method and system based on the Internet of Things, and relates to the technical field of water environment monitoring and safety alarming, and the main scheme is as follows: firstly, collecting water quality physical and chemical parameters and water quantity parameters of a current detection period, then respectively calculating each subentry index, and calculating the water quality index; comprising temperature, turbidity, conductivity index and the like obtained by physical parameters, dissolved oxygen, pH value, lead content index and the like obtained by chemical parameters, and water quantity index, so as to obtain water quality physical index, chemical index and water environment safety index, and then judge whether to trigger safety real-time alarm or not; and acquiring data of a previous cycle to calculate a historical water environment safety index and an index growth rate, and comparing with corresponding threshold values to judge whether to give a delayed alarm. According to the method, multi-parameter and multi-cycle data are integrated, a comprehensive and accurate judgment basis is provided for water environment safety monitoring, the monitoring accuracy and reliability are effectively improved, and the water environment safety is guaranteed.

The invention discloses an image processing method and device, electronic equipment and a medium, and belongs to the technical field of image processing. The image processing method provided by the embodiment of the invention comprises the following steps: a first image signal processor performs abnormal frame detection processing on an image frame transmitted by an image sensor to obtain a detection result; when the detection result indicates that the image frame is an abnormal image frame, the first image signal processor carries out restoration processing on the abnormal image frame to obtain a restored image frame; the restored image frame is transmitted to a second image signal processor.

The invention relates to the technical field of digital video devices, in particular to a digital video device control method and device. The method comprises the following steps: acquiring digital monitoring data, performing real-time scene acquisition and dynamic region optimization according to the data, and generating optimized dynamic region data; video frames are read based on the dynamic region optimization data, and abnormal behavior analysis and behavior intention evaluation are carried out; calculating the complexity of monitoring behaviors by combining video frame data and behavior intention data, and evaluating resource consumption increase; the processing bottleneck is evaluated according to the resource consumption growth data, and the processing capability attenuation analysis is carried out, so that the monitoring device is adjusted, and the system performance is optimized; according to the method, the resource allocation and processing capability of the video device is dynamically adjusted through real-time analysis and processing of monitoring data. According to the invention, the digital video device is adjusted, so that the prediction identification of the digital video device is more accurate and efficient.

The invention relates to the technical field of pipe network monitoring, in particular to a water supply pipe network pipe burst alarm positioning system based on real-time pressure monitoring data, and the system comprises a pressure data collection module which collects pressure data from a plurality of pressure monitoring points of a water supply pipe network; the pressure drop threshold calculation module is used for determining an alarm pressure drop threshold of each pressure monitoring point based on historical pressure data; the voltage drop alarm module is used for calculating a current time window voltage drop value and comparing the current time window voltage drop value with a voltage drop threshold value; the association alarm module is used for clustering alarm information of adjacent pressure monitoring points and combining a plurality of alarm points which are close in space into a single alarm event; the alarm positioning module is used for matching a pipe explosion node corresponding to the current alarm information by utilizing a mode recognition algorithm and quickly positioning the pipe explosion node; according to the method, the system calculates the aggregation characteristics, the description capability and the credibility of the alarm event are enhanced, and the identification and positioning efficiency of the system on the pipe explosion event in the complex pipe network environment is improved.

The invention provides a memory address generation method, a memory control method and a memory address generation device. The generation method comprises the following steps: obtaining a data memory address based on an address signal from a bus of the SOC system by utilizing a memory controller in the SOC system, and correspondingly storing data from the bus into a data memory unit according to an access sequence from the lowest bit to the highest bit of the data memory address; and based on the mode of the storage controller, obtaining the ECC memory address, and correspondingly storing the obtained ECC check codes into the ECC storage unit according to the access sequence from the highest bit to the lowest bit of the ECC memory address. Therefore, the problem of common cause failure of the address line can be avoided, and the function safety protection effect is improved.

The invention relates to an intelligent target identification domain, in particular to an intelligent attack target identification method and system fused with multi-source information, and the method comprises the steps: obtaining the distribution information of a shielding region through constructing a real-time three-dimensional model of an urban region, obtaining the feature information of an attack target through a data fusion method, and obtaining the feature information of the attack target; predicting a shielding area into which the strike target enters through spatial contrastive analysis; predicting a predicted moving track of the strike target in the shielding area through a dynamic filtering algorithm; obtaining the real-time position of the strike target in the shielding area through the predicted moving track and the feature information of the strike target; and acquiring detection data when the hit target leaves the occlusion area, obtaining an identification result through a feature matching algorithm, and when the identification result is that the hit target cannot be confirmed, triggering a correction mechanism by the target identification system. The problem that after a hit target enters a hidden area, it is difficult to continuously track and accurately lock the target, and consequently the hit precision is reduced or the target is lost is solved.

The invention discloses a lithium iron phosphate battery multi-class fault diagnosis method based on deep learning, and belongs to the field of lithium battery fault diagnosis. The sparrow search algorithm optimized by the eagle-Cauchy variant is used for self-adaptive updating of variational mode decomposition parameters, and the lightweight fault diagnosis framework fusing the convolutional neural network and Transform is applied to fault diagnosis, so that local and global features in battery time sequence data can be effectively captured; complex modes and interdependence existing in fault features are skillfully captured, diagnosis and detection of various tiny faults of the lithium battery are facilitated, the recognition capability of the tiny faults is enhanced, fault information can be timely and accurately recognized, and fault misdiagnosis is reduced.

The invention discloses a gas vortex refrigeration state regulation and control method and system, and aims to compensate energy loss and improve refrigeration efficiency, the method comprises the following steps: step 1, acquiring historical refrigeration data of a refrigerator, analyzing according to the historical refrigeration data of the refrigerator to obtain an energy loss condition in a gas vortex refrigeration process of the refrigerator, and calculating a gas vortex refrigeration state of the refrigerator; calculating to obtain an energy adjustment coefficient; and 2, gas vortex refrigeration parameters are obtained according to the energy adjustment coefficient, and energy loss in the gas vortex refrigeration process of the refrigerator is compensated.

The invention relates to the technical field of data monitoring, in particular to a concentrator data acquisition method and device, equipment and a medium. The method comprises the following steps: collecting monitoring data received by a concentrator; classifying the monitoring data into a plurality of groups of data according to the acquisition equipment, wherein each group of data corresponds to one acquisition equipment; determining a device type corresponding to the target data set, and inputting the target data set into a preset recognition model corresponding to the device type to obtain a recognition result about whether the target data set output by the preset recognition model is abnormal, the target data set being any one of multiple groups of data; if the identification result is that the target data group is abnormal, analyzing an abnormal reason; and generating a warning signal containing the abnormal reason, and sending the warning signal to terminal equipment corresponding to the related personnel. According to the invention, the data exception of the concentrator can be identified in time.

The embodiment of the invention discloses an intelligent forest fire identification method and device based on multi-feature fusion, equipment and a storage medium. The method comprises the steps that the smoke content and a power transmission inspection image of a target area are collected; extracting features related to time, and identifying dynamic behaviors of flames; extracting frequency-related features, and identifying the spectral characteristics of the flame; the method comprises the following steps: determining linear correlation between pixel values of two frames of power transmission inspection images; the area change rate and the mass center offset distance of the flame area are determined; constructing a thermodynamic diagram according to the dynamic behavior of the flame, the spectral characteristic of the flame, the linear correlation, the area change rate of the flame region and the mass center offset distance, and determining the feature with the maximum influence and the fusion allocation weight; and fusing the features by using an XGboost algorithm and carrying out intelligent forest fire identification. According to the scheme, the defects that a traditional forest fire recognition system is poor in robustness and low in accuracy are overcome, the forest fire recognition accuracy is improved, and the disaster prevention and reduction capacity and the operation and maintenance decision level of a power grid are improved.

The invention provides an abnormal gait correction method and device based on a foot electrostatic gait signal, and relates to the technical field of medical instruments. And performing preprocessing and feature point extraction on the signal by using variational mode decomposition and a self-adaptive dynamic threshold algorithm so as to establish gait fingerprint features of the user as a reference. Then, the gait is monitored in real time through multi-dimensional analysis, and once an abnormal gait is detected, a correction signal is generated according to gait fingerprint features; the device is composed of an electrostatic induction electrode, a signal processing module, a monitoring analysis module and a correction output module. The non-contact electrostatic induction method is adopted, discomfort caused by the fact that the sensor makes direct contact with the human body is avoided, meanwhile, the accuracy and timeliness of abnormal gait recognition are ensured by combining multi-dimensional analysis, the method is suitable for gait monitoring and correction in all weathers and all scenes, and the walking safety of a user is improved.

The invention relates to the technical field of sewage detection, and particularly discloses a pipeline internal sewage detection system for communication, which comprises an information acquisition module for acquiring sewage data of each preset point location of a pipeline and image information of each preset point location of the pipeline; the data processing module performs target feature screening on the sewage data of each preset point location to obtain abnormal sewage features, determines the acquisition point location of the abnormal sewage features as an abnormal point location and determines image information of the abnormal point location; carrying out risk analysis on the image information of each abnormal point location and the corresponding abnormal sewage features, and obtaining pipeline flow velocity parameters of the abnormal point locations; the analysis module performs pipeline sewage distribution analysis based on the abnormal sewage characteristics, the image information of the abnormal point locations and the pipeline flow velocity parameters of the abnormal point locations, and judges whether current pipeline problem information matching is correct or not according to a pipeline sewage distribution analysis result: if yes, the pipeline problem information is output; and if not, monitoring the current abnormal point location data in real time.

The invention relates to the technical field of oil and gas exploitation equipment, and provides a plunger pump packing state monitoring method and device.The method comprises the steps that the packing pressure of each packing cavity is obtained through a packing pressure detector installed at the position of the packing cavity in a fluid end valve box of a plunger pump; the discharge pressure of the fluid end valve box is obtained; and monitoring the packing state of the plunger pump according to the discharge pressure of the fluid end valve box and the packing pressure of each packing cavity. The method can timely and accurately identify the state of the packing, timely find the fault of the packing, timely maintain and protect the packing, reduce the damage of the fluid end valve box body caused by the damage of the packing, prolong the service life of the valve box, reduce the sudden fault shutdown of the plunger pump caused by the fault of the packing, improve the use efficiency of the plunger pump on site, and reduce the cost. Therefore, the plunger pump is more stable and reliable in operation, more targeted and more convenient to maintain, and the maintenance time and the maintenance cost are reduced.

The invention relates to the technical field of artificial intelligence, and discloses a phishing detection method and device, computer equipment and a storage medium. The phishing detection method comprises the following steps: acquiring network service data, and preprocessing the network service data to obtain network feature data; performing prediction processing on the network feature data through a preset phishing detection model to obtain a phishing detection result; the preset fishing detection model comprises optimized model parameters obtained after secretary bird algorithm optimization and back propagation iteration; and when it is confirmed that the fishing detection result is a fishing abnormal result, generating fishing early warning information according to the fishing detection result. According to the preset phishing detection model, the secretary bird optimization algorithm is introduced to optimize the model parameters, so that the model training efficiency and generalization ability are improved, the accuracy of phishing detection can be improved by utilizing the optimized model, the real-time performance of detection is ensured, the occurrence of false alarm and missing report can be reduced, and the phishing detection effect is ensured.

The invention discloses a real-time monitoring ice collapse early warning method. The method comprises the following steps: acquiring a glacier field image based on an image acquisition device; processing the glacier field image, and extracting an ice collapse range of the monitored area; based on the extracted ice collapse range of the monitoring area, ice collapse event occurrence risk prediction is carried out; and carrying out early warning grade division according to an ice collapse event occurrence risk prediction result. According to the invention, ice collapse can be effectively and timely identified.

The invention provides an image auditing method, device and equipment and a computer readable medium, and the method comprises the steps: carrying out the character detection of a to-be-audited image through calling a character detection model, cutting the to-be-audited image according to the detected characters, obtaining at least one character image, and carrying out the cutting of the to-be-audited image according to each character image, performing multi-dimensional feature detection on the character image, selecting a corresponding special text recognition model to perform text recognition on the character image according to a feature detection result to obtain a text recognition result, and performing violation judgment according to all the text recognition results corresponding to the to-be-audited image to obtain a violation judgment result. According to the method and the device, the to-be-audited image which is judged to be illegal is processed according to the preset illegal image processing rule, so that an image auditing solution which is high in automation degree, high in accuracy and high in processing speed is provided, the burden of manual auditing can be greatly reduced, and the accuracy and the efficiency of content auditing are improved.

The invention relates to the technical field of intelligent fire rescue, and discloses a small self-sensing intelligent special vehicle and a control method.The small self-sensing intelligent special vehicle is characterized in that a plurality of supporting foot devices are connected to the outer side face of a lower bottom plate, a gearbox is connected to the terminal of the top of the lower bottom plate, plunger pumps are connected to the output ends of the gearbox, and the number of the plunger pumps corresponds to that of the supporting foot devices; the top of the upper top plate is connected with a recognition device, and a spraying device corresponding to the fire extinguishing agent storage box is arranged at the front end of the top of the upper top plate; one side of the top end of the middle plate is connected with a fire extinguishing agent storage box, and the middle end of the top face of the middle plate is connected with a steering assembly. According to the device, the change of a fire source can be recognized in time in a complex environment, and accurate and effective fire extinguishing measures are taken. According to the device, a brand new thought and method are brought to forest fire fighting, the fire source checking and extinguishing efficiency is improved, secondary reburning of the fire is avoided, and the overall safety and emergency response capacity of fire fighting operation are improved.

The utility model provides a foot switch, and belongs to the technical field of medical instruments. And two pedal buttons and three pedals are arranged, so that the device can be matched with surgical equipment with various output modes for use. Due to the design of the two pedal buttons, switching of energy output modes can be achieved; the three pedals can respectively control on and off of a circuit, and the linear switch can realize linear control of output. And the three pedals adopt different colors, so that a user can quickly and timely identify and use a proper pedal. And the bosses are arranged between the pedals, so that a user can be prevented from treading and sliding or treading the two switches at the same time, and misoperation is avoided. According to the foot switch provided by the invention, switching of multiple modes and circuit control under single-mode operation can be completed conveniently and efficiently, and the operation is simple and very convenient.

The invention belongs to the technical field of electric digital data processing, and particularly relates to a PDC drill bit torsion impact signal collecting and processing method which comprises the steps that torsion data are segmented into a plurality of torsion data windows; determining the torsion waveform complexity of the torsion data window; determining the rotation speed slippage strength and the formation meshing complexity of the torsion data window; determining an adaptive order of an AR model of the torsion data window; and performing predictive filtering on the torsion data in the torsion data window to obtain a predictive residual sequence of the torsion data window, and extracting a torsion impact signal according to numerical characteristics of the predictive residual sequence. According to the method, the numerical characteristics of the torsion data and the rotating speed data are analyzed, the order of the AR model is self-adapted, the limitation of the AR model with the fixed order is overcome, background noise is filtered out, and the accuracy and robustness of the torsion impact signal are improved.

The utility model relates to the technical field of battery detection, in particular to a lithium battery ultrasonic welding fixture detection device, which comprises a detection plate, a detection module and a control module. Rectangular grooves are formed in the two side faces of the detection plate. A pressure sensor is fixed on the side surface of the rectangular groove; a push plate is connected into the rectangular groove through a translation structure; according to the utility model, the movable push plate and the pressure sensor are arranged on the two sides of the detection plate, so that the clamping elastic force of the ultrasonic welding fixture is detected; meanwhile, through a dovetail groove formed in the lower portion of the push plate, it can be guaranteed that the moving direction of the movable push plate is perpendicular to the plane of the push plate, and the parallelism of the clamp push plate is verified; by arranging the clamp elastic force detection mechanism, the clamping force of the ultrasonic welding clamp can be accurately read, the consistency of the elastic force of all the clamps is guaranteed, and in addition, when a clamp spring is damaged, identification can be conducted in time.

The invention relates to a method and system for identifying a fire combustion form of oil-filled equipment of a transformer substation, and the method comprises the steps: carrying out the binarization processing of an original image, and segmenting a combustion region; calculating the area, height, width, centroid, number of connected regions and other parameters of the segmented region image; and inputting the parameters into a pre-established and trained improved particle swarm algorithm-support vector machine model, and finally obtaining a classification identification result of the fire combustion form of the oil-filled equipment of the transformer substation. According to the improved particle swarm algorithm-support vector machine model, an evolution rule of a genetic algorithm is added in a traditional particle swarm algorithm, the ability of the particle swarm algorithm in the aspect of optimization is optimized, the probability of falling into a local optimal solution is low, iterative search optimization is conducted on support vector machine kernel parameters, and the optimal kernel parameters are obtained. The method can identify the combustion form in time at the early stage of fire combustion of the oil-filled equipment of the transformer substation, and assists fire-fighting departments and intelligent fire-fighting equipment to carry out targeted fire extinguishing operation.

A method for identifying the type of grounding fault based on a self-organizing competitive neural network belongs to the field of relay protection of power systems. It is characterized by comprising the following steps: step a, collecting the zero-sequence current after the fault and obtaining the power frequency component; step b, obtaining the amplitude change trend function of the power frequency component of the zero-sequence current; step c, extracting characteristic parameters; step d, if there is no unstable grounding grounding fault, execute step e; step e, construct a neural network based on a self-organizing competitive type; step f, using the neural network based on the self-organizing competitive type to determine whether a tree-line discharge fault or other types of stable grounding grounding fault occurs in the distribution network, and output it. In this method for identifying the type of grounding fault based on a self-organizing competitive neural network, a competitive neural network is used to identify the type of grounding fault in the distribution network, especially accurately identifying the tree-line discharge fault, filling the gap in this field.

This invention provides a method, device, electronic device, and storage medium for identifying road debris, belonging to the field of signal processing technology. The method includes: acquiring the original vibration signals corresponding to multiple grating measuring points in a grating array vibration sensing optical cable deployed on the road; determining a first energy for each grating measuring point within a first time window based on the original vibration signal; determining the occurrence time of a debris event within the first time window based on the first energy of each grating measuring point; determining a second energy for each grating measuring point within a second time window, wherein the occurrence time falls within the second time window; and determining the location of the debris on the road based on the second energy. Because the location of the debris is determined based on the energy distribution of multiple grating measuring points within a precise time window, the possibility of false alarms and missed alarms is reduced, improving the accuracy and efficiency of debris identification.

This invention discloses a semi-automatic assembly equipment for flexible copper busbar plugs. The flexible copper busbar plug includes a flexible copper busbar, a plug housing, and a sleeve. The equipment includes a rotary feeding mechanism with several carriers, and a first manual station, a second manual station, a positioning station, a crimping station, an inspection station, and a unloading station arranged sequentially along the rotation direction of the rotary feeding mechanism. The carriers are used to carry the flexible copper busbar plugs. The first manual station is used for pre-assembling the flexible copper busbar and plug housing onto the carrier. The second manual station is used for assembling the sleeve with the pre-assembled semi-finished products of the flexible copper busbar and plug housing mounted in the carrier. The unloading station includes an unloading clamping mechanism and an unloading conveying mechanism for unloading the finished flexible copper busbar plug. This invention adopts a semi-automatic design, combining manual pre-assembly with automated crimping and inspection, which reduces reliance on high-precision components and equipment costs, reduces the risk of frequent failures, and is more suitable for the production scenarios of small and medium-sized manufacturing enterprises.

The invention relates to the technical field of intelligent data analysis, in particular to an intelligent community fire-fighting big data analysis system and method based on artificial intelligence, and the system comprises a data collection module, an artificial intelligence module, a screening module, a first determination module, a calculation module, a second determination module, a judgment module, an alarm module and an adjustment module. By monitoring combustible heaps, fire hydrant water levels and water tank water pressures of all grids in a community in real time, the system can quickly identify potential fire risks and give an alarm in time, the probability of fire occurrence is reduced, the dynamic adjustment function of the system flexibly optimizes grid spacing according to the number of abnormal grids and a preset standard, and the safety of the system is improved. The configuration efficiency of fire-fighting resources is improved, so that a community can accumulate and analyze fire-fighting data, and the problems of low accuracy and slow response of a fire-fighting safety analysis result caused by single monitoring means and insufficient real-time processing capability are effectively solved.

This invention relates to the field of image processing technology and discloses a method and system for monitoring sewage outlets based on unmanned aerial vehicles (UAVs). The method includes: generating a dynamic flight strategy for the UAV based on meteorological information and lighting conditions of the target area; conducting preliminary monitoring of sewage outlets in the target area based on their distribution to obtain effective sewage outlet locations and images; filtering out ineffective sewage outlet locations from the distribution; compensating for ineffective sewage outlet locations using historical pollution data and a pre-set multi-period pollution model to generate a pollution heat map; extracting sewage outlet features from the effective sewage outlet images; marking anomalies in the target area's sewage outlets to obtain abnormal sewage outlet locations; conducting centralized inspections of these abnormal locations to obtain the monitoring results for the target sewage outlets. This invention effectively improves the stability of image acquisition quality, the comprehensiveness of sewage outlet location monitoring, and the accuracy of identification under extreme weather and lighting conditions.

The present invention relates to the technical field of chemical reaction equipment, and in particular to a pipeline reactor control system for high pressure, the system comprising a pipeline reactor, a data acquisition module, a data analysis module, a data prediction module and a safety monitoring module. The present invention installs pressure and temperature sensors in each block, so that the system can obtain more detailed and accurate parameter distribution inside the reactor, greatly improving the monitoring accuracy and spatial resolution of reaction parameters. The data analysis module determines the abnormal block by calculating the pressure index and temperature index, and dynamically adjusts the acquisition cycle or reaction parameters. The data prediction module predicts based on the adjusted abnormal parameter data, providing the system with a forward-looking judgment basis, which can effectively prevent safety hazards and improve production efficiency. At the same time, the safety monitoring module quickly identifies abnormal situations and takes corresponding early warning and suspension measures, which significantly improves the safety of the reaction process.

The invention discloses a supply chain data management method and system based on big data, and relates to the technical field of supply chain management. The method comprises the steps of obtaining historical transaction time sequence data of a to-be-supervised merchant, extracting daily transaction feature information in transaction data for normalization processing, taking the normalized daily transaction feature information time sequence data as an input sequence of an LSTM neural network, obtaining a first predicted value of the daily transaction feature information at the current moment by using the neural network, and obtaining a second predicted value of the daily transaction feature information at the current moment; constructing a statistical prediction model by using the normalized daily transaction feature information time series data, calculating a second prediction value of the current daily transaction feature information, performing weighting processing on the obtained first prediction value and the second prediction value, obtaining a weighted prediction value of the current daily transaction feature information, setting a prediction permissible error, and obtaining a prediction result of the current daily transaction feature information; and calculating a predicted value confidence interval of the daily transaction feature information, and judging an abnormal condition of the daily transaction feature information at the current moment according to the obtained confidence interval.

The present application relates to the technical field of edge computing, and particularly relates to a mine environment intelligent monitoring method and system based on edge computing, which comprises: collecting multi-dimensional environment data under a mine; dividing a mine space into multiple local spaces based on three-dimensional coordinates of each sensor under the mine; determining an abnormal state value of a local space in a preset time period by using multi-dimensional environment data of the local space in the preset time period; obtaining a first initial category center point by using the abnormal state value and multi-dimensional environment data of the local space in a previous time period; obtaining a second initial category center point by using multi-dimensional environment data of the local space in a subsequent time period; iteratively updating the first and second initial category center points by using a Mini-Batch K-Means algorithm to obtain a risk value of the local space; and performing mine risk early warning based on the risk value. The present application can improve the real-time performance of underground security.