519 results about "Parallel detection" patented technology

The invention relates to the technical field of artificial intelligence, in particular to a multi-mode agent RAG-ReAct double-engine cooperative training method. The method comprises the following steps: acquiring multi-modal data, and converting the multi-modal data into a high-dimensional vector; constructing a knowledge graph based on the high-dimensional vector; encoding the semantic relationship of the knowledge graph into a model fine adjustment gradient direction by using a dynamic distillation technology; designing a distributed architecture based on the high-dimensional vector; performing hybrid retrieval based on a distributed architecture to obtain hybrid retrieval data; executing distributed reasoning based on a preset recursive reflection mechanism and the mixed retrieval data to generate distributed reasoning data; performing data parallel detection according to the distributed reasoning data to obtain data parallel parameters; and performing node resource scheduling based on the data parallel parameters so as to obtain node load balancing data. Based on the artificial intelligence technology, the reasoning accuracy and the resource utilization rate of the multi-modal agent in a complex task environment are effectively improved.

The invention discloses a protocol self-adaption method and device for household photovoltaic data access, and the method comprises the steps: transmitting a predefined multi-protocol detection instruction set to a newly accessed household photovoltaic terminal when the access of the household photovoltaic terminal is detected for the first time, and the predefined multi-protocol detection instruction set comprises standardized instructions of at least two heterogeneous communication protocols; monitoring response data of the household photovoltaic terminal, and identifying a communication protocol type supported by the household photovoltaic terminal based on a feature matching relationship of the response data; dynamically generating a corresponding data analysis template based on the identified communication protocol type, and establishing a protocol adaptation channel; and data acquisition and analysis are carried out on the household photovoltaic terminal through the protocol adaptive channel, so that protocol adaptive access is realized. Parallel detection of heterogeneous protocols is realized by pre-defining a multi-protocol detection instruction set, non-preset library identification of protocol types is completed in combination with response feature matching, and edge-side adaptive access is achieved by using a dynamically generated analysis template and a protocol adaptive channel.

The invention discloses a good product visual identification method and system, a medium and a program product, and relates to the field of image data processing, and the method comprises the steps: determining the detection type of a to-be-traced product; the method comprises the following steps: when a process detection request is requested, acquiring abnormal process information for triggering detection, calculating a process correlation degree between each completed process and the abnormal process, and generating a to-be-detected sequence; in a final inspection request, extracting defect feature data of a to-be-traced product, determining an associated process set from the process flow database, and sequencing to generate a to-be-detected sequence; sequentially acquiring to-be-detected process images of each process ranked in the front from the to-be-detected sequence, and inputting the to-be-detected process images into a parallel detection channel for detection; when the result of the parallel detection channel is passing, updating the to-be-detected sequence, and obtaining a first process image for detection; and when the result of the parallel detection channel is abnormal, determining an abnormal process and stopping the detection. By implementing the method, the problem positioning efficiency based on visual identification can be improved.

The invention discloses an intelligent road disease detection method and equipment based on dynamic multi-scale convolution. The method comprises the following steps: performing target category labeling on a training sample set to form a corresponding relationship between sample data and labels; establishing a detection framework including a backbone network, a neck network and a detection network, the backbone network extracting features through convolution layer and cross-stage local layer stacking, introducing spatial pyramid pooling to enhance multi-scale expression, and enhancing fine-grained disease area attention by enhancing spatial attention; the neck network is integrated with dynamic multi-scale convolution and is matched with an upper sampling layer and a cross-stage local layer to fuse high and low layer features; the detection network is provided with a plurality of parallel detection heads, and disease detection is carried out corresponding to different scale feature maps. According to the method, rapid detection and accurate positioning of disease targets of various sizes can be realized, and the detection capability of sparse and small-scale disease targets is remarkably improved through collaborative optimization of a dynamic multi-scale convolution kernel and an enhanced space attention mechanism.

The invention discloses a patterned wafer defect detection system and method based on double-optical-comb space-time coding, and the system comprises a double-optical-comb light source module, a space coding illumination module, a time programming control module, a signal collection and processing module, and a data analysis and processing module. And the data analysis processing module receives and analyzes the restored three-dimensional shape information of the surface of the wafer to be detected, extracts defect related characteristics, establishes a mapping relation between spectral characteristics and defects, and identifies the defects on the surface of the wafer to obtain corresponding defect types and position information. According to the invention, a wafer defect detection technology based on double-optical-comb space-time coding is adopted, rapid scanning and parallel detection are realized by using a double-optical-comb interference technology, the detection efficiency is greatly improved, and absolute distance information of the wafer surface can be obtained. The result is fed back to the precise air floating motion platform, so that an automatic focusing optical path system and a feedback control part can be omitted, the detection efficiency is improved, and the size, complexity and cost of the whole wafer detection system are reduced.

The invention relates to the technical field of image analysis, in particular to an automatic marking robot calibration method based on visual guidance, which comprises the following steps of: establishing an image set comprising different resolution levels based on operation site image data acquired by a marking robot, and performing corner detection and straight line segment detection on each level image in parallel. According to the method, through a parallel detection mechanism of a multi-resolution hierarchical image set, angular point and straight line segment features under different scales are synchronously extracted, and a multi-scale feature point set with high robustness is constructed in combination with cross-hierarchical coordinate stability measurement and response intensity quantification. And performing dynamic screening and grouping association on the feature points based on a preset geometric constraint condition, eliminating noise interference and false detection features, and generating a candidate calibration structure set with spatial consistency. Weighted contribution value fitting is adopted, cross-scale stability and detection confidence of feature points are integrated, and geometric accuracy and anti-interference capability of calibration reference point coordinates are improved.

The invention relates to a medical image automatic labeling method and system based on multi-model voting, and the method comprises the steps: firstly obtaining a medical image, carrying out the standardized preprocessing, then carrying out the parallel detection of the preprocessed medical image through employing a plurality of target detection models of different architectures, and outputting a multi-model detection result. Thirdly, performing quality evaluation on the detection frame of each model through confidence normalization and detection frame weight calculation; finally, a decision strategy combining soft voting and hard voting is adopted, voting decision making is carried out on the position and the category of the detection frame of each detection target, and postprocessing optimization is carried out on a result. The invention further relates to a corresponding system, electronic equipment and a storage medium. According to the method, parallel detection is carried out by integrating the target detection models of various architectures, the accuracy and the labeling efficiency of automatic labeling of the medical image are remarkably improved by combining the frame quality score and the soft and hard voting decision strategy, and reliable technical support is provided for analysis of the medical image.

The invention provides a light guide plate surface defect detection method based on a GCC-YOLOv8 network model. The method comprises the following steps: acquiring an image of a light guide plate to be detected; the image is input into a GCC-YOLOv8 network model for defect detection; outputting surface defect information of the light guide plate based on a detection result; wherein the GCC-YOLOv8 network model is constructed based on a YOLOv8 network model, and comprises a backbone network unit, a neck network unit and a detection head network unit with three parallel detection branches, which are arranged in sequence; two GKA-C2f1 modules are arranged at the tail end of the backbone network unit; the neck network unit comprises an FPN network unit and a PAN network unit; the FPN network unit comprises two up-sampling modules, two C2f2 modules, two Concat modules, an SPPF module and a CPCA module; the PAN network unit comprises two CBS modules, two Concat modules, two C2f2 modules, three CPCA modules, a CFC module and an SFC module. The light guide plate surface defect detection method has good light guide plate surface defect detection performance and robustness, and also has good detection speed.

The invention relates to the technical field of blood chemical analysis, in particular to a parallel task scheduling optimization method and system for a blood detection process, and the method comprises the steps: correcting the attenuation rate of a temperature-sensitive component, a protein component and a light-sensitive component in blood according to real-time environmental parameters, and obtaining the corrected attenuation rate of the blood under the current environmental condition; calculating the concentration change rate of each detection item according to the correction attenuation rate, and establishing a multi-dimensional priority evaluation matrix in combination with the clinical importance coefficient and the detection time demand; identifying component pairs and reaction types of the interactive influence; establishing a coupling reaction kinetic model, and predicting the concentration value of each component at a set time point; sorting the to-be-detected items according to the multi-dimensional priority evaluation matrix; when the detection item combination contains mutual influence components, adjusting a detection sequence according to a coupling attenuation prediction result; and distributing the adjusted detection task to the corresponding parallel detection equipment according to the equipment compatibility, and generating and executing a task scheduling scheme.

The invention relates to the technical field of biomedicine, in particular to a primer probe combination for broad-spectrum screening of common respiratory pathogens and application of the primer probe combination. Through screening and optimization, the respiratory pathogen nucleic acid detection primer probe combination for broad-spectrum screening of common acute respiratory infection and community-acquired pneumonia is obtained; the primer probe combination covers 18 respiratory tract pathogens, has the advantages of high accuracy, good specificity, strong anti-interference capability, good inclusiveness, high sensitivity, hyperjoint detection and the like, can realize parallel detection of 20 targets in cooperation with totally-closed single-tube PCR amplification, greatly improves early diagnosis of respiratory tract diseases with complex pathogen structures, and has wide application prospects. The method has very important significance on prevention, control and treatment of infectious diseases.

The invention discloses a collision parallel detection method, device and equipment and a storage medium, and relates to the field of multi-body separation numerical simulation, and the method comprises the steps: carrying out the parallel partitioning of a multi-body separation object through the grid node coordinate information of the multi-body separation object determined based on a current simulated grid file, and obtaining a partitioning result; determining the number of target bounding boxes in each region based on the grid geometric information corresponding to each region in the partitioning result, and triggering a bounding box creation operation; after the bounding boxes are created, image areas corresponding to the bounding boxes in the areas are determined on the basis of a preset rule and the information of the isolates in the areas, and the bounding boxes to be detected are determined by judging whether the bounding boxes are overlapped with the bounding boxes in the corresponding image areas in space or not; and judging whether a grid intersection point exists between the target area corresponding to the bounding box to be detected and the corresponding image area or not so as to complete collision parallel detection. According to the invention, the efficiency and accuracy of collision detection are effectively improved.

The invention discloses an SAR image ship small target robust detection method and system based on improved YOLOv11, a storage medium and electronic equipment, and the method comprises the following steps: embedding an S2-MLPv2 module at the tail end of a backbone network, and suppressing speckle noise through spatial displacement operation and dynamic filtering; the SAFMN module is used for replacing traditional up-sampling, and the multi-scale feature fusion capability is enhanced in combination with a deformable convolution and gating mechanism; a SimAM non-parameter attention module is introduced, and ship texture and geometric features are adaptively focused based on an energy function; designing a TSIoU loss function, fusing a central point diagonal distance measurement and an end point distance measurement, and optimizing bounding box regression precision; a slice auxiliary reasoning strategy is applied, and the small target recall rate is increased through multi-scale slicing and parallel detection. The method solves the problems of strong noise interference, insufficient small target feature extraction, poor multi-scale adaptability and low convergence efficiency in the prior art.

The invention discloses an array type optical fiber ultrasonic imaging system based on multi-path delay parallel detection, and belongs to the technical field of ultrasonic detection and imaging. The main working process of the system is as follows: continuous laser output by a laser is modulated into pulse laser to be transmitted to an optical fiber ultrasonic detector array, and ultrasonic waves emitted by an ultrasonic wave generating device act on each detection array element to cause the property change of a detection optical signal; through multi-path time delay with the optical fiber as the medium, the detection optical signal forms different transmission time in each channel, is distinguished in a time division mode and is combined into a single path to be output. After photoelectric conversion, electric signals containing multi-channel detection information are collected in parallel, the signals are analyzed at a terminal processor by means of an imaging algorithm with an array parallel architecture, information of a to-be-detected target is obtained, and image reconstruction is completed. The array type optical fiber ultrasonic imaging system has the advantages of being accurate and rapid in scanning detection, simple and compact in system structure and efficient and high-quality in image reconstruction, and large-scale and low-cost array type optical fiber ultrasonic imaging is achieved.

The invention discloses an automobile off-line detection method. The method comprises the following steps: acquiring a plurality of automobile off-line detection items; determining a dependency relationship and a non-dependency relationship among the plurality of automobile offline detection items, and determining a detection process of the plurality of automobile offline detection items according to the dependency relationship and the non-dependency relationship, the detection process comprises the following steps: detecting automobile offline detection items with a dependency relationship in a serial detection mode, and detecting automobile offline detection items with a non-dependency relationship in a parallel detection mode; according to the method and the device, the plurality of detection items with the dependency relationship are detected in a serial manner, and the plurality of detection items with the non-dependency relationship are detected in a parallel manner, so that the plurality of detection items can be concentrated together for detection, the total time of automobile off-line detection is shortened, and the production efficiency is improved.

The invention provides a laser radar ranging method based on parallel detection, which relates to the technical field of laser radar ranging, and comprises the following steps: generating N lasers with different wavelengths, splitting beams by a beam splitter, separating optical channels with N independent wavelengths by a demultiplexer, performing frequency shift processing, combining N paths of optical signals after frequency shift through a beam combiner, and combining the N paths of optical signals after frequency shift through a beam splitter; generating a beam of composite reference light containing N wavelengths, wherein each wavelength has a specific frequency shift; signal light returned from a target and the composite reference light are jointly input into a coupler for frequency mixing so as to generate an optical beat frequency signal, and operation processing is performed to calculate the target distance and the target radial speed corresponding to each wavelength. According to the laser radar ranging method provided by the invention, parallel detection of the laser radar can be realized, beat frequency signals corresponding to a plurality of different wavelengths can be distinguished only by integrating a group of detectors, a cross-group amplifier and an analog-to-digital converter, the cost is greatly reduced, and the complexity of the system is reduced.

The invention relates to the field of airport runway detection, in particular to an airport runway pavement settlement monitoring method, device and equipment and a medium. The method comprises the following steps: acquiring the airport runway of the current airport, performing region division on the airport runway, and allocating a corresponding detection point to each region, so that parallel detection can be realized, the overall detection time can be greatly shortened, and the monitoring efficiency can be improved. Furthermore, each detection point is provided with a special detection device, so that the settlement condition of the point can be accurately measured. Furthermore, each detection point is provided with a special detection device, so that the settlement condition of the point can be accurately measured. By integrating the data of a plurality of detection points, the settlement degree of the whole runway can be evaluated more accurately, and the possibility of false report and missing report is reduced. In addition, the region division is also beneficial to identifying a specific region with relatively serious settlement, and accurate guidance is provided for subsequent maintenance and repair.

The invention discloses a detection kit and method for detecting allergic inflammatory factors based on a centrifugal micro-fluidic chip, and relates to the field of kits, the detection kit comprises the centrifugal micro-fluidic chip and a preset reagent; the centrifugal micro-fluidic chip comprises a chip base body, a sample adding cavity, a liquid level sensor, a liquid level sensor, a liquid level sensor, a liquid level sensor and a liquid level sensor, the nucleic acid extraction and purification cavity comprises a plurality of amplification cavities; a reagent storage chamber; a waste liquid chamber; and a capillary valve. The functions of nucleic acid extraction, variable temperature amplification and multi-target synchronous detection are integrated through the centrifugal micro-fluidic chip, full-closed operation is achieved by means of centrifugal force or other driving force such as air pressure and electroosmotic flow, and the pollution risk caused by manual step-by-step operation is thoroughly eliminated; a multi-wavelength distinguishable fluorescent probe and a grouping preset primer combination are adopted to synchronously complete parallel detection of the allergic inflammatory factor mRNA and the drug accompanying diagnosis target, cross interference is avoided, and large instruments and equipment do not need to be relied on.

The invention relates to the technical field of data processing and traceability, and discloses a traditional Chinese medicinal material full-link traceability anomaly detection method based on graph propagation and trust scoring, which comprises the following steps of: obtaining multi-source heterogeneous data of a traditional Chinese medicinal material full link, and constructing a traceability graph containing a traditional Chinese medicinal material circulation entity node and an associated edge relationship; performing parallel detection on the to-be-detected nodes in the traceability map by using a sub-model library to obtain original abnormal scores of multiple dimensions; and calculating an adaptive fusion weight according to the feature adaptation degree of the sub-model and the to-be-detected node. The adaptive fusion weight is introduced in the local anomaly refining step, so that the system can automatically improve the weight of the sub-model sensitive to the high-risk feature, and the system can dynamically recognize and preferentially acquire the optimal model for the current data feature; the problem that the traditional static fusion method is insufficient in robustness when facing diversified abnormal modes is avoided.

The invention relates to the technical field of body posture recognition, and particularly discloses an automatic body posture recognition system based on video analysis, which comprises a video acquisition module, a preprocessing module, a key point detection module, a skeleton tracking module, an action recognition module and a cross-view fusion module. According to the scheme, a parallel detection and information fusion mode is adopted, and the detection precision of the key points of the human body is remarkably improved by utilizing the accurate position of the high-resolution feature and the rich semantics of the low-resolution feature; the target is tracked according to the topological structure and the key point position of the human skeleton, the method has extremely high robustness for illumination and dressing changes, and a more stable and continuous human skeleton movement track is generated; the time sequence convolution and the space-time diagram convolution are combined to carry out joint modeling on the space and time double-domain features, complete action representation is obtained, and the stability, the accuracy and the adaptability of body posture recognition are effectively improved.

The invention discloses an intelligent obstacle recognition method for an unmanned loader, and aims to solve the problems of low obstacle recognition precision, poor real-time performance and insufficient coordination of obstacle avoidance and operation in a complex operation scene. The method comprises the following steps: establishing a multi-source sensing system such as a laser radar and a camera, and realizing data time sequence synchronization, space unification and noise optimization through FPGA hardware; obstacle space and category features are extracted through double-branch parallel detection, and a modal weight fusion result is dynamically distributed in combination with a scene; an improved Hungary algorithm and a classification tracking model are adopted to realize obstacle ID stable association and trajectory tracking, and trajectory prediction is completed based on LSTM and Kalman filtering; and a dynamic avoidance area is constructed, and an obstacle avoidance path is generated through global-local bilevel programming and a safety potential field algorithm and is corrected in real time. The obstacle recognition robustness and real-time performance in a complex environment are improved, and safe and efficient operation of the unmanned loader is guaranteed.

The invention discloses a semiconductor etching end point detection method and a multi-channel detection system, and the detection method comprises the following steps: outputting detection light; expanding the detection light into a continuous spectrum band; imaging the continuous spectral band to a modulation surface of a digital micromirror device, and establishing a pixel group corresponding to each narrow-band wavelength of the continuous spectral band; performing spatial sampling on the continuous spectrum band, and distributing and writing a unique radio frequency modulation frequency f for each pixel group; respectively executing vibration type switch modulation on each pixel group at a radio frequency modulation frequency f so as to convert wavelength information of a continuous spectrum band into a time domain frequency optical signal; converting the time domain frequency optical signal into a time domain electric signal; performing frequency domain analysis on the time domain electric signal, and demodulating gas absorption light intensity corresponding to each modulation frequency; and judging the etching end point of the semiconductor according to the change trend of the gas absorption light intensity. According to the invention, parallel detection of a plurality of spectrum channels can be realized through a single detector so as to judge an etching end point.

The invention discloses a medical image data management method and system based on artificial intelligence, and relates to the technical field of data management, and the method comprises the steps: collecting medical image data, converting the medical image data into an initial five-dimensional tensor, carrying out the preprocessing through a pulse neural network, and outputting a spatial-temporal characteristic spectrum; performing spatio-temporal joint feature face analysis on the spatio-temporal feature map by adopting an incremental feature space updating algorithm, extracting cross-slice features in combination with a three-dimensional convolution kernel, and generating a dimensionality reduction feature vector sequence; inputting the desensitization feature vector into a parallel detection network to generate a credibility scoring report; and triggering federal learning according to the credibility scoring report, dynamically updating the feature space parameters, and synchronizing the feature space parameters to the edge node through a secure communication protocol. Through high-dimensional tensor pulse coding and spatio-temporal joint feature analysis, the calculation efficiency and feature representation precision of the multi-modal medical image are improved.

The invention relates to the technical field of high-speed monitoring intelligent detection, in particular to a small target rapid enhancement detection method, device and equipment based on an image hot spot area and a medium. Intercepting an image slice through a hot spot region maintained by an asynchronous thread; forming an image array by the original image and the slice image, and inputting the image array into a target detection model supporting Batch reasoning for parallel detection; the slice detection result is mapped back to an original image coordinate system and then fused, and a final detection result is output; in the asynchronous thread, based on a detection result, screening a focused target, reversely calculating a candidate slice window and marking the candidate slice window to the difference graph; when conditions are met, the difference graph is constructed into an integral graph, the area with the highest heat degree is positioned as a new hot spot area, hot spot information is smoothly updated in combination with a sliding window mechanism, the optimal detection area can be accurately extracted, redundant calculation is reduced, the detection efficiency and the small target recognition rate are improved, and high-quality support is provided for follow-up tracking and analysis.

The invention belongs to the technical field of space environment detection, and relates to an ASIC (application specific integrated circuit)-based multichannel parallel charge detection device for space charged particle detection, which comprises an anode plate, a front placing plate and a digital acquisition and processing plate, and signal transmission is performed between the anode plate and the front placing plate as well as between the front placing plate and the digital acquisition and processing plate through inter-plate connectors; the anode plate is an annular anode and is divided into a plurality of anodes, the output of each anode is connected with a current-limiting resistor in series, and a charge signal output by each anode is transmitted to the front amplifier plate through the inter-plate connector; the pre-amplification board adopts an integrated charge sensitive amplifier ASIC to amplify multiple paths of charge signals output by the anode plate, and outputs multiple paths of digital pulses to be input into the digital acquisition processing board through an inter-board connector; and the digital acquisition and processing board is used for detecting and counting multiple paths of digital pulses output by the pre-amplification board in parallel, and periodically packaging and outputting the digital pulses to a PC (Personal Computer) end.

The invention discloses a physicochemical index detection method for canned food production, and relates to the technical field of food detection, and the method comprises the following steps: deploying annularly arranged near infrared spectrum, eddy current electromagnetic and biological impedance three-mode sensing units, and collecting tank reflection spectrum, eddy current loss and complex impedance response signals in a non-contact manner; extracting characteristic parameters of sulfur dioxide, heavy metals and microorganisms through wavelet packet decomposition, Hilbert transform and principal component analysis; constructing a ternary coupling correction equation set to perform cross correction on the original data, and outputting an accurate detection result; and a self-calibration mechanism and an abnormal product automatic sorting function are integrated. By constructing four core technology systems of multi-modal synchronous acquisition, cross-modal feature coupling, dynamic threshold decision and online self-calibration, lossless, efficient and multi-index parallel detection is realized, the precision is stable, the cost is reduced by 60%, and the real-time quality control and tracing requirements of a full-automatic production line are met.

The present invention relates to the field of glass fiber cloth detection, and in particular to a device and method for detecting the longitude and latitude of low dielectric glass fiber cloth. It includes a detection platform, a limit component, a glue coating component and a detection component. The detection platform table top is connected to the top seat through a support column, and a hollow space is set inside the detection platform; the limit component is arranged on the detection platform, and the upper and lower ends are both provided with limit ends for limiting the flatness of the low dielectric glass fiber cloth, and both sets of limit ends can be adjusted by rotating the origin and turning the whole; the glue coating component is arranged in the hollow of the detection platform, and the flatness of the low dielectric glass fiber cloth is assisted by gluing the upper limit end; the detection component is raised and lowered between the top seat and the limit component. The present invention detects the longitude and latitude of the low dielectric glass fiber cloth by cooperating with the glue coating component and the detection component through a double-sided limit component. The detection process is coherent and automatic, with a lot of parallel detection data, a large detection range, a stable detection environment, and efficient and accurate detection.

The invention relates to the technical field of high-energy physics and particle detection, and discloses a cosmic ray muon detector based on non-optical coupling, the cosmic ray muon detector comprises a plurality of detection layers, each detection layer comprises a plurality of columns of detection units arranged in parallel, each column of detection units is formed by aligning and arranging a plurality of scintillator strips end to end in a mechanical mode, and optical coupling does not exist between the adjacent scintillator strips; each detection unit is provided with a wavelength shift optical fiber group, and the wavelength shift optical fiber groups are fixed through mechanical clamping grooves formed in the side faces of the scintillator strips and are continuously laid along the whole column of detection units; the light emitting end of the wavelength shift optical fiber group is coupled to the photoelectric conversion module. According to the invention, mechanical splicing without optical coupling is adopted to construct a long detection column, so that the detector can be quickly assembled and deployed on site by using a standard assembly which is low in cost and easy to transport, and high cost, high risk and complex process of precise optical coupling of a long-strip-shaped scintillator are thoroughly avoided.

According to the parallel detection method, the CIS test board card and the semiconductor test equipment provided by the invention, the probe station is controlled to move so as to connect the probe connector on the probe station with the pin of the chip to be tested, and the light source system is controlled to provide the test light source for the chip to be tested; controlling a switching circuit to switch a signal detected by the probe station to an image test system or a DC test system; acquiring detection information of the chip to be tested acquired by the image test system or the DC test system; and testing the to-be-tested chip based on the detection information, so that automatic testing can be realized, and the testing time can be saved.

The application discloses a dual-channel parallel detection airborne phased array radar ground moving target extraction method, comprising the following steps: acquiring coherent pulse train data of an airborne phased array radar; forming sum-difference beam data according to preset sum-difference beam weighting coefficients; constructing a pulse compression matching function to perform pulse compression on the sum-difference beam data; performing platform motion compensation and pulse Doppler processing on the pulse compressed sum-difference beam data; performing high-speed channel determination according to amplitude statistical results of each Doppler unit of the sum beam, and performing CFAR detection; performing low-speed channel determination according to the amplitude statistical results of each Doppler unit of the sum beam, processing low-speed channel data by using sum-difference 3DT-STAP algorithm, and performing CFAR detection and amplitude normalization processing on the processed data; merging the CFAR detection results of the high-speed channel data and the CFAR detection and amplitude normalization processing results of the low-speed channel data, and extracting a point track.

The utility model provides a current detection circuit and a current detection system, the current detection circuit is used for parallel current detection of at least two to-be-detected pins of a to-be-detected device, the current detection circuit comprises a detection module and a plurality of matched resistors, and the detection module comprises a plurality of detection terminals; the to-be-tested device is connected with the power supply device, the to-be-tested device comprises at least two to-be-tested pins, and the power supply device supplies power to the to-be-tested device; each matched resistor is connected in series between the positive terminal of the power supply device and one end of the corresponding to-be-tested pin, and is used for converting a first voltage output by the power supply device into a second voltage; each detection terminal is connected to an intermediate node after the matching resistor and the corresponding to-be-detected pin are connected in series so as to acquire a second voltage. According to the current detection circuit provided by the utility model, the current value of each pin to be detected can be detected at one time through the detection module and the matched resistor, the test efficiency is improved, the instability caused by a relay is avoided, and the test stability is improved.