38results about How to "Ensure identification" patented technology

The invention discloses a classroom teaching quality auxiliary evaluation method, device and system based on cloud edge collaboration, and the method comprises the steps: obtaining video image data, collected by a camera, of students in a classroom in real time, and transmitting the video image data to an edge device; and in the edge device, performing student classroom behavior identification and tracking on the received video image data by using a pre-trained student classroom behavior identification model, and fusing the identification results of adjacent multiple frames with the identification result of the current frame in the tracking process to obtain the final identification result of the current frame. Evaluating the classroom teaching quality according to the student classroom behavior recognition result, wherein the student classroom behavior recognition model is obtained by training based on a multi-label target detection algorithm and by adopting a knowledge distillation structure in advance; and sending a student classroom behavior identification result and an evaluation result to a cloud video server for storage. The method has the advantages of being simple in implementation method, low in cost, high in real-time performance and precision, high in robustness and the like.

According to the radar moving target detection method based on small sample transfer learning and the attention mechanism, a small amount of actual radar echoes and high-fidelity scene simulation data are used as a data training set, and direct detection of a moving target under a strong clutter background can be realized. An attention mechanism is introduced by utilizing the point / line distribution characteristics of a moving target and a scene clutter in a space-time two-dimensional plane, the clutter sorting and recognition capability of the network can be ensured while the network training speed is improved, meanwhile, a small sample migration twin network is further combined, a small amount of actually measured data can be utilized to correct a target detection network, and the target detection accuracy is improved. The adaptability of the network to actual working conditions is improved, and the generalization ability is improved. According to airborne flight calibration test data analysis, under the condition of a low signal-clutter-noise ratio, compared with a traditional STAP + CFAR technology, the method has higher detection performance and lower false alarm rate. Meanwhile, based on network pre-training and a small sample transfer learning mechanism, the method has practicability in the aspect of early warning radar real-time processing and has high engineering application value.

The invention discloses a PCB production line defect detection analysis method and system based on an identification model, particularly relates to the technical field of PCB quality detection, and is used for solving the problems of low PCB defect detection precision and high omission ratio in the prior art. The method comprises the following steps: scanning the whole surface of a PCB to generate a thickness distribution diagram, identifying a thickness non-uniform region by adopting a threshold segmentation algorithm, and marking a key detection region; potential defects irrelevant to process fluctuation are identified through historical production data analysis; and adjusting the polarization angle of the light source to analyze the reflectivity difference, and identifying the hidden defect area in combination with the microstructure of the material. The union set of the potential defect area and the hidden defect area is marked as the suspected incomplete area, and the influence of the suspected incomplete area on the performance of the PCB is analyzed according to the quality standard, so that the detection precision and comprehensiveness are improved, and the high-quality control of the PCB is realized.

The invention provides a self-discovery ad-hoc network control method based on a swan-gap operating system, and relates to the technical field of data processing, and the method comprises the steps: based on the swan-gap operating system, extracting the identification information and communication parameters of equipment from all to-be-networked equipment; carrying out compatibility judgment on the communication protocol version and the maximum support bandwidth between the devices; determining a control relationship between the master device and the slave device; dynamically allocating communication resources to each device by adopting a distributed channel allocation algorithm in combination with task requirements of the devices and network bandwidth information; periodically synchronizing the resource state of each device; when the slave device sends a networking quitting request, the master device updates the networking candidate data and re-determines the networking control relationship after releasing the corresponding communication resources; when the master device exits, the device with the highest priority is selected from the slave devices to automatically take over the control role and complete the reconstruction of the networking control relation; according to the invention, the autonomy and accuracy of networking are improved.

The invention relates to the technical field of computer security, in particular to an embedded software reinforcement method based on a national cryptographic algorithm. The method comprises the following steps: generating a key pair by using SM2, constructing a code dependency graph in combination with spectral clustering, and optimizing key distribution; carrying out segmented encryption by adopting SM4 symmetric encryption, and identifying sensitive code snippets; the integrity of the embedded software is monitored and responded in real time through SM3 hash check and anomaly detection; analyzing vulnerability distribution based on random forest regression by using an SM4 encryption update package and dynamically adjusting an update frequency; and encrypting an operation log through SM3, and identifying a risk trend and generating an operation report by using a variational auto-encoder. According to the invention, through a plurality of technical means such as hierarchical encryption, real-time monitoring, dynamic key management and risk prediction, an efficient, dynamic and intelligent security reinforcement scheme is provided for embedded software, and the overall security and stability of the system are improved.

The invention discloses a customer service response evaluation method and system based on a large language model, and relates to the technical field of customer service responsiveness, and the method comprises the steps: obtaining customer service dialogue data, carrying out the emotion score assignment through an emotion dictionary and an emotion label library, and obtaining a customer service emotion text; obtaining a plurality of groups of different emotion threshold combinations, and layering the customer service emotion text to obtain the emotion level of the customer; acquiring emotion matching data and customer emotion change data; constructing an emotion threshold evaluation model based on machine learning; constructing an effect display model, carrying out data analysis on the effect display model to obtain an optimized emotion threshold combination, and applying the optimized emotion threshold combination to customer service response evaluation; according to the method and the device, the problem that the customer satisfaction and the service quality are finally influenced due to inaccurate analysis result and unstable service effect caused by experience-based setting of a sentiment analysis threshold value is solved.

The invention provides a hospital risk internal control automatic early warning system and method based on big data. The method comprises the following steps: determining a subimage block set corresponding to each image frame in a pedestrian monitoring video; selecting one image frame as a selected image frame, and determining a plurality of motion attitude vectors according to a sub-image block set corresponding to the selected image frame and the adjacent image frame, constructing a co-occurrence matrix of pedestrian attitudes on a motion amplitude and a co-occurrence matrix of pedestrian attitudes on a motion direction through all the motion attitude vectors, and obtaining pedestrian trajectory features at the corresponding moments of the selected image frames, so as to obtain pedestrian trajectory features at the corresponding moments of all the image frames; determining behavior feedback values of pedestrians in the hospital infection department aisle at the current moment based on all pedestrian trajectory features; and sending an abnormal behavior early warning signal to the hospital control center based on the behavior feedback value. According to the scheme, abnormal behavior feature recognition can be carried out on the target monitoring area when the monitoring picture is dynamically shielded.

The invention discloses a civil aviation fault monitoring method and system based on artificial intelligence. The method comprises the steps of data acquisition, preprocessing, delay function construction, civil aviation fault monitoring model establishment and civil aviation fault monitoring. The invention belongs to the field of fault monitoring, and particularly relates to a civil aviation fault monitoring method and system based on artificial intelligence, and the method comprises the steps: introducing a dynamic delay dynamic neural network equation, and simulating the dynamic behavior of a civil aviation system; a plurality of delay intervals are adopted to describe delay characteristics under different working conditions, a more comprehensive state monitoring means is provided for civil aviation, and the safety and stability of civil aviation operation are ensured; whether the civil aviation system is in a normal operation state or not is judged through a delay passivity criterion, and fault monitoring fast decision is achieved; by combining delay, dynamic adjustment and passivity criteria, the state of the civil aviation system can be comprehensively evaluated, the capability of coping with a complex fault mode is provided, and then the fault monitoring efficiency is improved.

The invention provides a module and a method for automatically detecting Pt-derived superfine defects of ultra-thin glass, and a system and a method for automatically detecting poor appearance of ultra-thin glass by using the module. The automatic inspection system for appearance defects comprises: an edge inspection module for inspecting peeling or wedge-shaped defects when physically cutting ultra-thin glass in units of units; the surface inspection module is used for inspecting scratches, cracks or foreign matters on the surface area of the ultra-thin glass; the phase measurement light deflection inspection module is used for measuring and inspecting stains or recesses in the surface area of the ultra-thin glass through phase change; and a Pt-derived ultrafine defect inspection module for inspecting Pt-derived ultrafine defects generated in the production process of the ultra-thin glass mother substrate. The Pt derivative superfine defect inspection module comprises a holographic restoration device; the scanning device is used for moving the holographic restoration device to a preset scanning path; and an ultra-fine defect determination means for determining Pt-derived ultra-fine defects by a machine learning algorithm using the inherent Moore pattern of the image obtained from the holographic restoration means.

The utility model discloses an intelligent inspection robot image recognition device, and relates to the technical field of intelligent inspection robots. The robot comprises a robot body and a rotating plate arranged above the robot body, wherein a lifting mechanism, a rotating mechanism and an adjusting mechanism are arranged on the robot body; and the lifting mechanism comprises two first connecting assemblies, two second connecting assemblies, two third connecting assemblies and a power output assembly, each first connecting assembly comprises a first supporting plate fixedly installed on the top of the rotating plate, and two first toothed connecting blocks are rotationally installed on the front face of each first supporting plate. Through the arrangement of the lifting mechanism, the problems that target objects at certain specific angles or heights cannot be captured, the comprehensive monitoring and recognition capability of the robot to the environment is affected, and when the height of the camera needs to be changed to cover a wider area, the camera which cannot ascend and descend limits the inspection range of the robot are solved.

The invention relates to the technical field of image recognition, and discloses a multi-commodity automatic correction and recognition method and an intelligent self-service cash register, and the method comprises the steps: extracting a commodity front image from a commodity image; identifying label areas, and selecting associated feature points for each label area; segmenting a commodity area from the front image of the commodity; determining an associated commodity area of each label area; extracting a commodity side image, and determining an associated commodity area of each label area in the commodity side image; reconstructing a three-dimensional model of the commodity; scanning a label area to obtain commodity information, and verifying the commodity information based on a gravity sensor and the three-dimensional model of the commodity; according to the invention, the accuracy of commodity identification and the settlement efficiency are improved, the user experience is improved, the security and reliability of the system are enhanced, and a more efficient, convenient and reliable self-service cashier solution is provided.

The invention relates to the technical field of data processing, and discloses a digital scene driven intelligent public service platform construction method and device and a medium. The method comprises the following steps: performing four-dimensional mapping on a business scene to obtain a scene element list; performing value flow analysis and capability decomposition on the scene element list to obtain a scene value flow graph and a scene capability map; converting the scene value flow graph and the scene capability map into a four-layer architecture model through hierarchical processing; carrying out asset classification on the four-layer architecture model to obtain information type and design type asset models; performing componentization processing on the asset model to form three types of component libraries; and integrating the component library into an intelligent public service platform through micro-service integration. The problems of multi-dimensional mapping of digital scene elements and associated tracing of scene assets are solved, so that the efficiency of constructing an intelligent public service platform driven by a digital scene is improved.

The invention relates to the technical field of garbage treatment, in particular to a garbage classification intelligent detection device which comprises a box body, box doors, a mounting frame, placement plates, a first collection box and the like, the box doors are hinged to the left side and the right side of the box body, the mounting frame is connected to the bottom in the box body, and the three placement plates are connected to the mounting frame; and first collecting boxes for collecting garbage are placed on the placing plates. Garbage can be grabbed through the mechanical arm and put into the first collecting box or the second collecting box to be classified and collected, plastic bottles can be punctured through steel needles, water or beverages in the plastic bottles can flow into a material receiving box, the water and the beverages are prevented from falling into the second collecting box, and therefore the surrounding environment can be protected; water and beverages in the material receiving box can be heated through the heater, so that the water and the beverages are evaporated to form water vapor, the water vapor is condensed into water drops after being cooled, the water is discharged through the water discharging pipe, the water can be recycled, and waste is avoided.

The invention relates to the field of clutch control, and discloses a clutch execution control method, which comprises the following steps of: performing CAN (Controller Area Network) communication between a gearbox controller and an engine controller, an anti-lock braking system and an instrument, and acquiring information such as the rotating speed of an input shaft, the rotating speed of an output shaft, oil temperature, a gear sensor, a pedal displacement sensor and a driving mode of a gearbox sensor; the following functions are achieved: a driver can control the clutch to be opened and closed through hand pinching in a manual mode. In the automatic mode, smooth starting can be completed without operating a clutch, and the clutch is automatically opened at a low rotating speed to avoid flameout of an engine. In the automatic mode, smooth and fast gear shifting can be completed only by operating a gear shifting rod without operating a clutch, gear shifting can be achieved without closing an accelerator, and galloping of an engine is avoided. In the automatic mode, a driver can open the clutch at any time through hand pinching, and intervention on a transmission chain at any time is achieved. In the automatic mode, the TCU can remind a driver that the gear is too low or too high.

This application relates to a short-circuit detection device for battery cells in a winding machine, belonging to the field of battery cell detection technology. To solve the problem of short-circuited battery cells occupying processing time, it includes a detection mechanism arranged on one side of the cold pressing plate of the winding machine. The detection mechanism includes a support frame, a transverse carrier plate, a lifting drive cylinder, a detection plate body, and a detection probe assembly. The support frame is arranged on one side of the cold pressing plate, the transverse carrier plate is arranged on the support frame, the lifting drive cylinder is vertically connected to the transverse carrier plate, and the piston end of the lifting drive cylinder is connected to the detection plate body. The detection probe assembly includes positive electrode detection probes and negative electrode detection probes, which are distributed on the end face of the detection plate body. The support frame is provided with a transverse movement mechanism for driving the transverse carrier plate. This application effectively ensures that short-circuited battery cells are effectively identified and rejected before hot pressing, preventing short-circuited battery cells from occupying hot pressing equipment time and significantly improving production efficiency.

The present invention discloses a hysteroscopic liquid management leakage detection method and system, which specifically relates to the field of hysteroscopic surgery liquid management, and is used to solve the problem of real-time monitoring leakage. The method is to set the sampling frequency and the moving average method, collect pressure data in real time, dynamically update the lag time using the cross-correlation function and the sliding window, build a prediction model to improve the pressure prediction accuracy, dynamically adjust the perfusion pump output using the PID algorithm, continuously monitor and calculate the pressure difference and lag time in real time, generate corresponding signals, divide the lag time series to calculate the lag time complexity index, combine the pressure fluctuation asymmetry index, and comprehensively quantify the system disorder and abnormal pressure fluctuation, provide in-depth risk assessment, and ensure the safety of surgery. Through multi-level evaluation and real-time adjustment, it is possible to accurately monitor the liquid management status, identify potential leakage risks, ensure the smooth progress of surgery and patient safety, and improve system reliability and surgery quality.

The utility model relates to hub surface detection technical field especially relates to a kind of hub surface defect automatic identification device.The utility model provides such a kind of hub surface defect automatic identification device, it further includes workbench, cabinet door, electric guide rail, sliding seat, servo motor and placement seat, two cabinet doors are rotationally provided in workbench left side, electric guide rail is installed in workbench top, sliding seat is slidingly set on electric guide rail, servo motor is installed in sliding seat bottom, placement seat is rotationally set in sliding seat top, the output shaft of servo motor is connected with placement seat.By the telescopic rod of control cylinder drive top frame ascension, top frame ascension extrudes mounting rod and moves outward, drives top block to adhere to hub inner wall, when top block and hub are completely contacted, hub is automatically locked, so as to be fixed on placement seat, realize the quick locking hub of different sizes, improve the convenience and applicability of device use.

The invention provides an electric power communication data network field fault patrol device, and relates to the technical field of electric power communication, the electric power communication data network field fault patrol device comprises a traction rope, a mounting plate and a supporting plate, the left end of the mounting plate is fixedly connected with a fixing assembly, the bottom end of the supporting plate is fixedly provided with a detection assembly, and one side of the top end of the supporting plate is fixedly provided with a supporting rod. A module assembly is fixedly installed on the other side of the top end of the supporting plate, a fixing block is fixedly connected to the top end of the supporting rod, a solar panel is fixedly connected to the top end of the fixing block, and two alarm sensors are fixedly connected to the front side and the rear side of the top end of the supporting plate at equal intervals. The fault patrol device is installed at a deployed network key point through the installation plate and the fixing assembly, the traction rope ensures the installation stability of the device, the detection assembly and the module assembly collect and process the operation information of an electric power communication data network in real time, the solar panel continuously supplies power to the device, and normal operation of the fault patrol device is ensured.

The invention relates to a system for enabling migration and portability of legacy application software on the basis of application situations, in particular medical application situations, comprising: an input unit which is designed to read in data relating to a configuration of the legacy application software and a plurality of legacy service applications; a policy determination unit, which is configured to automatically determine a runtime architecture associated with the selected application situation on the basis of the read-in data; a transportability unit configured to load a transportability requirement based on the determined runtime architecture, the transportability requirement comprising a new service application linked to the legacy service application; a configuration unit configured to provide a new configuration of the legacy application software on the basis of the loaded transportability requirements; and an output unit which is designed to execute the application situation using the new configuration of the legacy application software.

The application of the invention is a divisional application of 202080104861.8. A data transmission method and a terminal device, capable of implementing inactive state data transmission based on a pre-configured uplink resource or a random access resource, the method comprising: if the terminal device selects to perform inactive state data transmission based on a random access resource, the terminal device sends uplink information to a network device, the uplink information at least comprises uplink data to be sent by the terminal equipment and identification information of the terminal equipment.

The invention provides an L5 / S1 minimally invasive intervertebral disc radiofrequency ablation operation path planning method and device based on an MR image. The method comprises the following steps that MRI data of an object in a DICOM format are obtained; preprocessing the data; modeling a path planning environment, and inputting data into a modeled model to obtain a modeled path planning environment; according to the obtained path planning environment, performing tetrahedron subdivision on the to-be-ablated 3D sub-model of the protrusion of intervertebral disc, and selecting an effective ablation target spot; constructing a candidate path; performing hard constraint condition detection and soft constraint condition detection on the candidate path to obtain an optimal path; and outputting the path planning environment and the optimal path. And the determined operation path can reduce the operation risk and the occurrence rate of complications while ensuring the operation effect. Clinical information such as cross section angulation, coronal plane angulation and sagittal plane angulation is additionally provided, and working efficiency and decision quality are improved.

The utility model discloses a baby clothes, including clothes body, the inside of clothes body is provided with the protective sheet of cotton, one end of protective sheet with clothes body is fixed through the sewing line, the other end with clothes body is connected through magic tape, and the protective sheet towards the one side of clothes body is provided with the washing label. Through the protective sheet to the washing label is shielded protection, reduces the risk of washing label curl and damage, at the same time, the waterproof film is set up on the washing label, reduces the risk of washing label on the writing blur, to guarantee that user can discern to the information on washing label.

The invention is suitable for the technical field of electric power maintenance, and provides a ground wire flaw detection assembly based on photonic crystal elastic waves, the ground wire flaw detection assembly based on photonic crystal elastic waves comprises: a cabinet, in which is arranged a flaw detection host, a data collector, a power amplifier and a charge amplifier; the placement shell is fixed on one side of the case, and a first flaw detection clamp and a second flaw detection clamp are arranged in the placement shell; the sensor and the exciter are arranged on the first flaw detection clamp and the second flaw detection clamp respectively, the exciter is used for generating photonic crystal elastic waves, and the sensor is used for receiving photonic crystal elastic wave signals reflected by a ground wire. The ground wire flaw detection assembly based on the photonic crystal elastic waves solves the technical problems that a traditional ground wire detection method is low in efficiency, and tiny internal defects are difficult to find.

The present invention relates to the field of voltage control technology, and in particular to an automatic voltage control method based on active power trend determination. The method comprises the following steps: performing multi-scale monitoring data acquisition according to a power grid management system to generate multi-scale power matrix data; performing new energy output power prediction according to the multi-scale power matrix data to generate new energy power prediction data; performing spatiotemporal node similarity processing according to the multi-scale power matrix data, and performing node voltage change trend analysis to obtain node voltage trend data; performing power grid voltage stability margin assessment according to the new energy power prediction data and the node voltage trend data to generate voltage stability margin assessment data; performing intelligent voltage regulation instruction generation based on the voltage stability margin assessment data to obtain intelligent voltage regulation instruction data. The present invention realizes automatic control of power grid voltage through power grid voltage stability margin assessment, and effectively solves the voltage stability problem caused by new energy output fluctuations.

The present application discloses an information processing method, device, equipment and storage medium. The method includes obtaining a user access request, determining whether there is an identification model corresponding to the user access request; if it is determined that there is an identification model corresponding to the user access request, identifying and marking whether the user access request is an access request of a forged user through a historical user verification module; if it is determined that there is no identification model corresponding to the user access request, identifying and marking whether the user access request is an access request of a forged user through an initial user verification module. This solves the problem in the related art that illegal crawlers cause system anomalies of website servers and affect normal user access.

The invention provides a mental load assessment method based on human intelligence, an edge computing device and a medium. The method comprises the following steps: determining a candidate electrode cluster under each neighborhood scale based on an electroencephalogram electrode topological structure of electroencephalogram acquisition equipment worn by a to-be-tested person; according to the electroencephalogram sample under each load dimension, calculating a classification accuracy matrix of each candidate electrode cluster under the load dimension, and determining a sensitive electrode cluster under the load dimension based on the classification accuracy matrix; according to the corresponding local electroencephalogram sample of the sensitive electrode cluster under each load level in each load dimension, determining an electroencephalogram feature template, and forming an electroencephalogram feature template set; and according to the electroencephalogram data stream of the to-be-tested person and the electroencephalogram feature template set, determining the mental load index of the to-be-tested person. According to the invention, comprehensive evaluation and refined identification of the mental load of the personnel in a multi-load dimension can be ensured.

The utility model discloses an audible and visual alarm of an emergency fire alarm system, which comprises an audible and visual alarm body and a mounting mechanism for mounting the audible and visual alarm body, a light source module is arranged at the top of the audible and visual alarm body, and a sound source module is arranged on the front side of the audible and visual alarm body. When the audible and visual alarm body is installed, the angle can be adjusted in the horizontal direction by adjusting the position of the sliding base on the arc-shaped protruding part and fixing the sliding base through the first locking piece, and the angle can be adjusted in the horizontal direction by adjusting the position of the movable end on the arc-shaped hole and fixing the movable end through the second locking piece. The angle of the audible and visual alarm body is adjusted along the vertical direction. Finally, the installation orientation can be adjusted to ensure that the light signals and the sound can be recognized by people in a main activity area.

The present invention discloses a civil aviation fault monitoring method and system based on artificial intelligence, and the method includes data collection, preprocessing, constructing a delay function, establishing a civil aviation fault monitoring model and civil aviation fault monitoring. The present invention belongs to the field of fault monitoring, and specifically refers to a civil aviation fault monitoring method and system based on artificial intelligence. This scheme simulates the dynamic behavior of the civil aviation system by introducing a dynamic delay dynamic neural network equation; uses multiple delay intervals to describe the delay characteristics under different working conditions, and provides a more comprehensive state monitoring method for civil aviation to ensure the safety and stability of civil aviation operations; through the delay passivity criterion, it is judged whether the civil aviation system is in a normal operating state, and a rapid decision-making for fault monitoring is achieved; by combining delay, dynamic adjustment and passivity criteria, it can comprehensively evaluate the state of the civil aviation system, provide the ability to respond to complex fault modes, and thus improve the efficiency of fault monitoring.

The embodiment of the invention provides a method and device for identifying a defect or a flange based on a magnetic flux leakage signal, and the method comprises the steps: detecting a to-be-identified pipeline through a magnetic flux leakage detection instrument, and obtaining the abnormal data of the magnetic flux leakage signal; performing invalid elimination and blank filling processing on the abnormal data of the magnetic flux leakage signal to obtain an abnormal data segment; carrying out feature value extraction on the abnormal data segment to obtain a plurality of feature values used for expressing a magnetic flux leakage signal waveform; selecting a first characteristic value and a second characteristic value in the plurality of characteristic values; inputting the first characteristic value into a trained defect and flange identification model to obtain whether the to-be-identified pipeline has defects or flanges; and when the to-be-identified pipeline has defects, inputting the second characteristic value into a trained defect type identification model to obtain a defect type corresponding to the to-be-identified pipeline. According to the embodiment of the invention, defects or flanges can be identified, so that the health condition of the pipeline can be effectively detected.

The application discloses a coding and decoding method for error correction of a resistant nanopore channel, comprising the following steps: step S1: constructing a resistant nanopore channel composed of two-stage channels in series, processing a q-element string through the resistant nanopore channel to obtain an output l‑ mer sequence; step S2: performing gap detection and consistency recovery on the output l mer sequence; step S3: defining an error correction problem and converting the error correction problem into a detection and recovery problem of periodic fragment length change; step S4: analyzing uniqueness under an editing channel through editing alignment between two equal-length sequences, identifying a calibration fragment and constructing a summary of the calibration fragment length; and step S5: realizing error correction of the resistant nanopore channel through coding and decoding. The application converts the error correction problem into the detection and recovery problem of the periodic fragment length change, and realizes comprehensive channel error correction.