927results about How to "Resolve detection" patented technology

An anomaly detection and resolution system (ADRS) is disclosed for automatically detecting and resolving anomalies in computing environments. The ADRS may be implemented using an anomaly classification system defining different types of anomalies (e.g., a defined anomaly and an undefined anomaly). A defined anomaly may be based on bounds (fixed or seasonal) on any metric to be monitored. An anomaly detection and resolution component (ADRC) may be implemented in each component defining a service in a computing system. An ADRC may be configured to detect and attempt to resolve an anomaly locally. If the anomaly event for an anomaly can be resolved in the component, the ADRC may communicate the anomaly event to an ADRC of a parent component, if one exists. Each ADRC in a component may be configured to locally handle specific types of anomalies to reduce communication time and resource usage for resolving anomalies.

The invention relates to a nuclear power plant working robot and a control system thereof, which belong to the fields of robots and automation equipment. The nuclear power plant working robot is a crawler-type mobile manipulator and is composed of a mobile platform driven by double crawlers and a four freedom degree manipulator carried on the mobile platform. The nuclear power plant working robot can move inside a nuclear power plant, has two control modes of manual remote control and autonomous control, and is remotely controlled by a wireless or wired mode. The control system of the nuclear power plant working robot includes a host monitoring and planning control system and a robot control system which are matched to control the operation of the robot. The robot operates autonomously, is safe and reliable, can complete certain dangerous tasks in high radioactivity environment; the robot has small size, stable performance, great maneuverability and low operation cost; a crawler-type chassis has strong gripping force as well as certain climbing and obstacle clearing ability, and is suitable to walking over complex terrains; and the robot has high intelligence degree and can realize autonomous control and manual remote control.

The invention discloses a method for identifying virtual malicious nodes and a virtual malicious node network in social networks. The method includes the following steps: (1) obtaining the attribute data, behavioral data and communication data of unidentified accounts from target social networks; (2) calculating the creditworthiness of each unidentified account according to the credibility model feature vector calculated using the extracted data; (3) making a comparison between the user behavioral habit statistic data of each unidentified account with the credibility being lower than a set threshold value and that of a normal user, and judging whether the unidentified account is a virtual malicious node or not; (4) sorting the virtual malicious node set and establishing association for the virtual malicious node in each sort result, so as to form a virtual malicious node network; then evaluating by using the Bayesian network algorithm to determine the final virtual malicious node network. The method provided by the invention can be used to effectively identify highly-disguised malicious nodes and a collaborative virtual malicious node network.

A device for detecting an azimuth has a transmission array antenna having plural transmission antenna elements arrayed along an array axis and a receiving array antenna having plural receiving antenna elements arrayed along the array axis. A reception signal is acquired for each of channels by transmitting and receiving a search wave through each of the channels. The channels are arbitrary combinations of each of the transmission antenna elements and each of the receiving antenna elements. A first spatial frequency analysis is performed along the array axis of either ones of the transmission antenna elements and the receiving antenna elements using the reception signal. A second spatial frequency analysis is then performed along the array axis of the other ones of the antenna elements using results of the first spatial frequency analysis. An azimuth of a target is determined based on analysis results from the second spatial frequency analysis.

The invention discloses a method for detecting the error of a program control flow and intends to overcome the difficulties in increasing the detection rate of the errors of the control flow by the conventional method and solve the difficult problem with the detection of the control flow inside basic blocks and among processes. The method comprises the following steps according to the technical scheme of the invention: firstly, identifying basic blocks and confirming the routing relation among the basic blocks; then, confirming the maximum required number of detection commands of the internal control flow of each basic block according to the requirements of the internal control flow and the constitutive characteristics of the basic blocks; designing the labels for the basic blocks on that basis, and distributing the unique static label to each basic block; then, inserting the detection commands into the head part, inner part and tail part of each basic block in the program respectively, re-assembling and re-linking the assembler of the inserted detection commands of the control flow, and generating an executable program for achieving the detection of the error of the control flow.

The invention belongs to the technical field of oil and gas development, and discloses an evaluation method for hydrogen containing ingredient, porosity and aperture of shale rich in organic matters.According to the magnetic resonance imaging T1-T2 spectrum differences of kerogen, kerogen containing adsorption oil, different minerals containing aqueous clay, shale, shale dry samples, saturated oil rock samples and saturated water rock samples, a division scheme of each hydrogen containing ingredient of the shale rich in organic matters, and a quantitative expression method of fluid ingredients is established; the T2 spectrum of the saturated shale rich in organic matters is taken as a target, the T2 spectrum of the shale dry sample is taken as a substrate, substrate inversion is carried out to obtain the T2 spectrum of oil in a hole, and on the basis of the T2 spectrum, the porosity and the pore diameter distribution of the shale rich in organic matters is evaluated. Compared with theconventional method, the evaluation method disclosed by the invention is characterized in that high innovation and credibility can be shown, and is favorable for improving the analysis of the magnetic resonance imaging on an aspect of shale rock physical measurement.

The invention discloses a method for detecting the lack of a refrigerant in an air conditioner, and the air conditioner. The air conditioner comprises a compressor, a heat exchange, a throttling piece, a wind wheel and a controller, wherein an inlet refrigerant temperature sensor for detecting the refrigerant inlet temperature of the heat exchanger is arranged at an inlet of the heat exchanger; an outlet refrigerant temperature sensor for detecting the refrigerant outlet temperature of the heat exchanger is arranged at the outlet of the heat exchanger; when the absolute value of the difference between the refrigerant inlet temperature and the refrigerant outlet temperature is greater than or equal to a set value, the controller displays the lack of the refrigerant in the air conditioner. According to the invention, through detection by the relevant temperature sensors, the lack of the refrigerant in the air conditioner can be displayed timely and accurately, and the corresponding alarm information about the lack of the refrigerant can be given, therefore, the problem that the conventional household air conditioner cannot automatically detect the leakage or lack of the refrigerant is solved; and further, the method and the air conditioner have the characteristics of economy, practicability, and convenience and accuracy in operation.

The invention discloses a segmentation combination-based adaptive constant false alarm rate target detection method for an SAR (synthetic aperture radar) image, and belongs to the technical field of synthetic aperture radars. The method comprises the following steps of: dividing a reference window into four sub windows, extracting uniformity statistics of the four sub windows and judging whether the sub windows are uniform; obtaining parameters for estimating background clutter models by adopting corresponding sub window combination strategies according to the non-uniform number, and then obtaining a detection threshold value by using a false alarm probability and a relationship between the clutter models; and comparing the pixel value of a current detection unit with the detection threshold value, judging whether a target exists, detecting the whole SAR image to be detected by adopting a running water form, and performing target fusion operation on the detected SAR image. According to the scheme, the method has low calculation quantity and simple operation, solves the problems of low detection probability, high false alarm rate and the like when the environment is complex and changeable and multiple targets are adjacent under high-resolution large scenes in the prior art, obviously improves the detection effect, and can keep good detection performance under various complex detection environments.

The invention provides a conduit leakage inspection and safety early warning test system for oil-gas transportation conduit leakage inspection and corresponding early warning technique tests, relating to mechanical vibration or sound wave impact metering and conduit system technique field, comprising a circular conduit (53) which is connecting with a storage can region (2) and a pump (9) to be a close ring, is imbedded underground, is antiseptically treated and is cathodic protected, and a circular optical fibre (54) imbedded in one ditch. The circular conduit (53) has two lead-out branch pipes and a plurality of relief valve wells. A plurality of valves, volume meters and pressure instruments are provided on the conduit. An air compressor (26) is provided on one lead-out branch pipe, and another lead-out branch pipe is unloaded. A plurality of valves, pressure instruments and gas flowmeters are provided on the circular conduit. Pressure sensors of a circular conduit on-site instruments and remote transmitting cables transmit pressure signals to a central control room.

An anomaly detection and resolution system (ADRS) is disclosed for automatically detecting and resolving anomalies in computing environments. The ADRS may be implemented using an anomaly classification system defining different types of anomalies (e.g., a defined anomaly and an undefined anomaly). A defined anomaly may be based on bounds (fixed or seasonal) on any metric to be monitored. An anomaly detection and resolution component (ADRC) may be implemented in each component defining a service in a computing system. An ADRC may be configured to detect and attempt to resolve an anomaly locally. If the anomaly event for an anomaly can be resolved in the component, the ADRC may communicate the anomaly event to an ADRC of a parent component, if one exists. Each ADRC in a component may be configured to locally handle specific types of anomalies to reduce communication time and resource usage for resolving anomalies.

The invention discloses an image detection method and system based on a convolutional neural network, and the method comprises the following steps of collecting and obtaining a preset number of labeled sample images, and carrying out the preprocessing of the labeled sample images, and obtaining a denoised target area image with a label; performing feature extraction on each labeled target area image to obtain a 3D feature matrix of each labeled target area; training the 3D convolutional neural network weak entity detection model by using all the obtained 3D feature matrixes to obtain a trained3D convolutional neural network weak entity detection model; and performing preprocessing and feature extraction on the to-be-detected picture to obtain a 3D feature matrix of the to-be-detected picture, inputting the 3D feature matrix of the to-be-detected picture into the trained 3D convolutional neural network weak entity detection model, and outputting a detection result of the to-be-detectedpicture, so that the weak entity target detection can be completed more accurately and quickly.

The invention relates to a watt-hour meter inspection device and method, in particular relating to a three-phase intelligent watt-hour meter inspection device and method. The existing watt-hour inspection device has long inspection time, and needs an extra instrument for inspecting the power consumption of a watt-hour meter; the existing watt-hour meter inspection device has low efficiency and can inspect only one watt-hour meter for one time; and in addition, the existing watt-hour meter inspection device is inability of testing the cost-control trip of the intelligent watt-hour meter. The three-phase intelligent watt-hour meter inspection device comprises a standard clock, a three-phase program-controlled source, a standard watt-hour meter, error calculators, power consumption testers and a communication module. The inspection device provided by the invention is characterized by comprising trip monitoring units, wherein the inspection device has a plurality of testing work positions; one power consumption tester, one trip monitoring unit and one error calculator are arranged on each testing work position; each error calculator is provided with a counting calculation module; and the standard watt-hour meter is used for metering an output module. The three-phase intelligent watt-hour meter inspection device is rapid in inspecting watt-hour meters, and can inspect the power consumption and the cost-control trip.

A full-automatic baggage treating system for airpot features that an equal probability filling method is used to ensure that the baggage comes on the conveying belt with equal probability and the lattice method and the tracking-loating technique based on BIT stream are used for ensuring the precise tracking to each baggage.

The invention provides a method and device for detecting a thermal bulb of a compressor and an outdoor unit. The method for detecting the thermal bulb of the compressor includes the steps of acquiring temperature of the outdoor environment and the temperature of an exhaust port measured by the thermal bulb of the compressor, calculating the difference value between the temperature of the exhaust port and the temperature of the outdoor environment, judging whether the difference value is smaller than a first threshold, and determining the fact that the thermal bulb of the compressor drops when difference value is smaller than the first threshold. According to the technical scheme, the problem that the situation that the thermal bulb of the compressor drops can not be detected in time is solved, then prompts can be effectively given out under the situation that the thermal bulb drops, the phenomenon that the cooling effect or the heating effect of an air conditioner is reduced because the exhaust temperature of the compressor is inaccurately measured is avoided, and potential hazards of operation faults of an air conditioner unit are eliminated.

The invention relates to an automatic coin quality detection device, in particular to a high-precision multifunctional automatic coin production quality detection device based on a visual system, realized by applying an image processing technology. The detection device comprises a combined illumination imaging light source, an image receiving unit, an image processing unit, a result output unit and a damaged coin discarding device, wherein the image receiving unit, the image processing unit, the result output unit and the damaged coin discarding device are sequentially connected; the combined illumination imaging light source adopts a mode that surface light sources at a high angle and a low angle illuminate at the same time; the image receiving unit collects image data on the coin surface; the image processing unit process the collected image data, confirms and discards defective products according to a production standard and transfers real-time detected images and analysis result information to the result output unit; and the damaged coin discarding device discards the ultra-detective products according to the control information of the image processing unit. The invention has the objective detection standard, can quantitatively prescribe, improves the production efficiency, can continuously upgrade the detection speed, emancipates the labor force and improves the automation degree of coinage.

The invention relates to an intelligent patrol inspection robot and a patrol inspection method for an overhead transmission line. The intelligent patrol inspection robot of the invention has the following abnormal conditions: measuring the distance between a conductor and a peripheral tree, identifying the conductor broken strand, lightning strike point, self-explosion of an insulator, inclinationof a pole and tower, bird's nest and the like by means of images; The fault point can be located by GPS, and the distance can be located by the roller with high precision. Robot autonomous data processing and background auxiliary processing, human-machine interaction with the ground patrol personnel and intelligent overcoming obstacles in progress and other functions. The robot adopts remote wireless monitoring and control, can climb over obstacles independently and carry various detection equipment to complete the detection work. In power-saving mode, only one walking wheel can be used as adriving wheel to drive the robot to move, thus prolonging the endurance time. The robot of the invention integrates a plurality of technologies, thereby solving the bottleneck that the patrol robot has single function and is difficult to be applied to the actual detection, and deepening the application of the robot technology in the electric power patrol inspection work.

The invention relates to an analysis method and a system of metagenome data. According to the invention, a preliminary species identification result of a sample is obtained on the basis of a k-Mer algorithm, a part or all of supporting sequences are extracted on the basis of the preliminary species identification result, and the preliminary species identification result is verified by using a blast algorithm to judge whether the preliminary species identification result is a reported detected species or not. The method and system disclosed by the invention can lower false positivity, quickly and accurately obtain the reported detected species of the sample in a short time, and are compatible with various mainstream sequencing platforms, thereby being suitable for second-generation sequencing technologies and third sequencing technologies; the method and system of the invention can also accurately identify drug-resistant genes and drug-resistant mutation sites of the sample and map thedrug-resistant genes and the drug-resistant mutation sites of the sample to the reported detected species. Furthermore, the system disclosed by the invention can be used for identifying pathogenic microorganisms, especially endocarditis pathogens to overcome the defect that the endocarditis pathogens are difficultly cultured.

The invention discloses an automatic parking space detection and identification system based on a panorama visual assistance system. The system mainly aims at solving the problems that in the prior art, since a parking space marking line is not complete and there is shade or an obstacle in the parking space, the parking space detection and identification are inaccurate. The system includes a camera detection module and an identification module, and cameras are installed around a vehicle to splice a seamless panorama image capable of reflecting the surrounding information of the vehicle; the detection module completes detection of a parking space marking line and completes the parking space detection according to the perimeter of the marking line under the situation that the parking space marking line is not complete; the identification module completes the parking space identification by calculating a difference value of the gray level change in the parking space and the height of an obstacle in the parking space under the situation that there is shade or the obstacle in the parking space, and the detection and identification results are displayed on a vehicle-mounted central control big screen. The automatic parking space detection and identification system achieves efficient and accurate detection and identification of the parking space, provides effective bases for route planning and route tracking control in later automatic parking, and can be used by a driver to find a parking position.

The invention discloses a text detection method, system and device based on multi-receptive field depth characteristics and a medium, and the method comprises the steps: obtaining a text detection database, and taking the text detection database as a network training database; building a multi-receptive field depth network model; inputting a natural scene text picture and corresponding textbox coordinate true value data in the network training database into a multi-receptive field depth network model for training; calculating an image mask for segmentation through the trained multi-receptive field depth network model to obtain a segmentation result, and converting the segmentation region into a regression textbox coordinate; and counting the textbox size of the network training database, designing a textbox filtering condition, and screening out a target textbox according to the textbox filtering condition. The method fully utilizes the feature learning capability and classification performance of the deep network model, combines the characteristics of image segmentation, has the characteristics of high detection accuracy, high recall rate, strong robustness and the like, and has agood text detection effect in a natural scene.

The invention belongs to the technical field of concrete structure damage detection, and discloses a multi-target crack recognition method based on a YOLOv3 deep learning algorithm, which comprises the following steps: importing a crack image into a YOLOv3 model, and automatically compressing the image into 416 * 416 pixel resolution; dividing the original image into S * S grids according to the scale size of the feature map by adopting an up-sampling and feature fusion mode similar to FPN; taking the cross-to-parallel ratio of the candidate box and the real box as an evaluation criterion, and; performing K-means clustering analysis on mark boxes for all crack target marking boxes of the image training set to obtain the size of a candidate box; and predicting the probability that the frame contains the target for each boundary frame through logistic regression. According to the method, the complexity of network training is simplified, and the operation cost is reduced; according to the method, the multiple targets are quickly and accurately identified, the accuracy far superior to that of other models is obtained while the target detection is quickly realized, and the method has higher robustness and generalization capability and is more suitable for an engineering application environment.

The invention discloses a high-density flexible substrate appearance defect detection system and a method based on depth learning, the method comprises a hardware platform and a software detection platform. The detecting method comprises the following steps: FICS images containing different defects is collected as training samples; pre-processing on the sample image is carried out, including the unified standard size and manual marking of the defect position and the category in the sample; a sample image is input into a depth learning model based on an improved YOLO convolution neural networkto carry out training to obtain model parameters which are output as defect positions and categories; the collected image size is standardized and input into a trained depth learning model for detection, and the defect position and the category information in the acquired image are obtained. The method can achieve the rapid positioning and the type identification of the appearance defects of the high-density flexible substrate, and solves the problem that the traditional defect detection system and the method are low in speed and is difficult to achieve high-density FICS appearance defects.

A motor control apparatus of the present invention comprises a velocity detector for detecting a rotation velocity of a mover of a motor, a position detector for detecting a position of the mover, a differentiator for performing time differentiation with respect to the position detected by the position detector to obtain a velocity calculation value, a feedback signal generating circuit for generating a feedback signal including the velocity detection value obtained by the velocity detector as a high frequency component and the velocity calculation value obtained by the differentiator as a low frequency component, and a linear amplifier and an integrating amplifier for performing PI control, the linear amplifier and the integrating amplifier performing linear amplification and integrating amplification of a difference between the velocity instruction and the feedback signal, respectively.

The invention relates to an electronic document security ensuring system and an electronic document security ensuring method. The electronic document security ensuring system is based on a client server system, and performs user permission management by coordination of a client and a server, wherein the server comprises a document server module, a certificate management service module, a document log service module, a document distribution service module, and a user role and permission management service module; and the client comprises an electronic document editor module and a client configuration interface module. The electronic document security ensuring method comprises the following steps: 1) providing a unified security verification mode for different document formats of the client; 2) acquiring user operating information by operating information interception technology, and shielding corresponding unauthorized operation according to user permission; and 3) separating functions of document storage, role management, permission control and log management, and constructing an electronic document server end based on Web. The system and the method can prevent the information leakage in the form of electronic document and provide high security guarantee for electronic office.

An ultra-precise processing integrated platform for a large caliber optical mirror surface solves the problem that processing and detection are not integrated while processing the large caliber optical mirror surface in the conventional optical processing technical field. The platform integrates grinding, detection and milling for processing the large caliber optical mirror surface, and comprises a base, a horizontal moving device, a rotation device, an ultra-precise grinding device, a mirror surface detection device and a milling device, wherein the horizontal moving device drives the rotation device to move to a workpiece mounting position, a grinding position, a surface shape detection position and a milling position in a horizontal direction; a vertical moving device drives a spindle system and a diamond grinding wheel to move up and down in a vertical direction; the spindle system can adjust the inclined angle of the diamond grinding wheel; the diamond grinding wheel has a cambered surface; when in the workpiece mounting position, the rotation device can conduct cambered surface grinding on a workpiece; when in the surface shape detection position, the rotation device can conduct surface shape detection on the workpiece; when in the surface shape detection position, the rotation device can mill the workpiece.

The invention discloses a water meter disc area detection method based on full convolution recurrent neural network. The method comprises the following steps: obtaining the image of a water meter; marking the external rectangular frame at the water meter disc area on the image of the water meter; obtaining the marking information of the external rectangular frame at the water meter disc area; constructing a full convolution recurrent neural network; extracting the multi-channel characteristic image of the water meter image; using a sliding window to scan the multi-channel characteristic image; screening a candidate window for the meter disc area; extracting the corresponding position characteristics of the candidate window for the meter disc area; obtaining a final target detection result; and using the losses of the candidate window for the meter disc area and the final target to update the parameters for the full convolution recurrent neural network. According to the invention, the full convolution recurrent neural network in deep learning is utilized to automatically extract the characteristics of the water meter disc, which solves the problem of detecting a water meter disc area under a complicated environment and further inputs the identified position of the disc as identified by the water meter. This manner greatly increases the identification rate of a water meter.

The invention belongs to the field of optics and discloses a system for detecting abnormity of a proxy cache cluster based on automatic computing, for solving the problem that the existing proxy cluster system is huge in scale and serious results are caused if abnormity cannot be detected in time. The system comprises a state detection module, a state self-sensing module, a state self-recovery module and an algorithm execution module, wherein the state detection module is used for monitoring the state of distributed proxy clusters so as to acquire detailed data when the distributed proxy clusters runs; the state self-sensing module is used for receiving state data provided by the state detection module, analyzing the state data and identifying the running state of the current distributed proxy clusters; the state self-recovery module is used for judging parameters to be adjusted and adjustment degrees according to the running state results acquired by the state self-sensing module, and transmitting a parameter adjustment command to the algorithm execution module; the algorithm execution module is used for executing the parameter adjustment command transmitted by the state self-recovery module and dynamically changing running parameters. The system is applied to a proxy cluster system.

The invention discloses an anti-fading washing control method. According to the method, an impeller type washing machine or a tumbling-box washing machine is included. The washing machine comprises a washing machine body. An inner washing barrel, an outer barrel and a controller are mounted in the washing machine body. The washing machine body is further provided with a color sensor. The color sensor detects washing water in the washing process and feeds back a detected result to the controller. The controller analyzes the detected result and then judges whether follow-up operation for preventing fading and mutual dyeing needs to be conducted. The anti-fading washing machine can judge whether woven products fade or not accurately in time.

The invention discloses a lane line detection method based on a deep segmentation network. The method comprises the steps of 1, obtaining a normalized original image set and a lane line instance segmentation gray level image set; 2, constructing a multi-layer depth segmentation network, and training to obtain an optimal multi-layer depth segmentation network; 3, obtaining a lane line binary imageand a background binary image; 4, obtaining a feature map of the to-be-predicted road image; 5, obtaining a feature map of the to-be-predicted road image; 6, obtaining a lane line instance segmented image; And 7, obtaining a detection result map of the lane line. The method can effectively solve the problem of lane line change, can be suitable for lane line detection under various complex road conditions, and improves the robustness and real-time performance of detection.

The invention relates to a magnetic suspension device capable of automatically rotating, which comprises a bracket, an electromagnet, a suspension component, a displacement sensor, a rotating action coil, and a control circuit, wherein the electromagnet is arranged in the bracket; a suspension permanent magnet and a rotating permanent magnet are arranged in the suspension component; the suspension component can be magnetically suspended under the electromagnet and can automatically rotate along two directions; the displacement sensor is arranged in the bracket; the rotating action coil is arranged in the bracket and is used for detecting the rotation of the suspension component and driving the suspension component to rotate; the control circuit is arranged in the bracket; and the electromagnet, the displacement sensor and the rotating action coil are all connected to the control circuit. The suspension component of the device can stably suspend in air and can automatically rotate along two directions, the suspension component is under the power-off protection, and the control structure is simple. The device has the advantages of low power consumption, less emitted heat, and longerservice life. The device provided by the invention can be used as handiwork, showing stand, and the like.