150 results about "Probe array" patented technology

The invention relates to the technical field of LED display testing, in particular to an LED screen backlight module performance testing system which comprises the steps that a model identification code of a module to be tested is received, a corresponding standard optical parameter set is called, and initial driving current is applied. Actual optical signals driven by different currents are collected through a light sensing probe array and converted into a digital optical measurement value set containing brightness and color coordinate values of multiple sampling points. Comparing the brightness measurement value with a target brightness value point by point to generate a brightness consistency deviation matrix; and judging the color coordinate measurement value and the target range, and generating a color coordinate qualified identifier list. And calculating an actual brightness uniformity value according to the deviation matrix, and comparing the actual brightness uniformity value with a standard threshold value. And if the actual brightness uniformity value reaches the standard and the color coordinates of all the sampling points are qualified, generating a module qualification judgment instruction. According to the system, automatic and high-precision comprehensive evaluation of brightness space uniformity and chromaticity consistency of the backlight module is realized.

The invention discloses an impedance self-adaptive high-frequency probe, a probe card and a testing method, and belongs to the technical field of semiconductor high-frequency testing. When an existing high-frequency probe is used for a crimping test, a conductive probe body of the existing high-frequency probe generates mechanical deformation due to contact pressure, so that characteristic impedance is deviated, and uneven pressure in an array causes impedance dispersion. The problem is solved by coating the outer side of the conductive needle body with a composite material layer, the composite material layer is composed of an elastic insulating matrix and metal particles dispersed in the elastic insulating matrix, the metal particles have bimodal particle size distribution, and the total integral number is 30%-40%. When the probe is pressed, the composite material layer is subjected to reversible microscopic reconstruction, the effective dielectric constant of the composite material layer is increased along with the increase of the pressure, and the generated impedance change can counteract the impedance drop caused by the deformation of the conductive probe body, so that the self-adaptive stability of the characteristic impedance of the probe under the change of the pressure is realized. The impedance dispersion of the probe array can be remarkably reduced, the consistency and precision of high-frequency testing are improved, and the process compatibility is good.

The invention discloses a multi-frequency-point multi-polarization parallel sampling phased-array antenna automatic calibration platform, and relates to the technical field of antennas, and the platform comprises a control center which is used for parameter modeling and test task configuration, parallel sampling strategy generation, and data storage and tracing; the FPGA hardware controller is used for generating synchronous trigger pulses and synchronously triggering the radio frequency switch matrix, the multi-path signal source, the six-axis mechanical arm, the multi-probe array module, the double-axis rotary table and the vector network analyzer VNA array; the radio frequency switch matrix is used for quickly switching multi-frequency-point multi-polarization signals; the multi-path signal source, the six-axis mechanical arm, the multi-probe array module, the double-axis rotary table and the vector network analyzer VNA array are used for synchronously collecting test data; the data processing module is used for receiving the sampling data, calculating a test index and carrying out closed-loop compensation and result verification; the test efficiency, the equipment utilization rate and the measurement precision are remarkably improved, and full-process automatic calibration and multi-dimensional data tracing are achieved.

The invention discloses an elevator steel belt damage detection and quantitative analysis system based on eddy current and magnetic flux leakage dual-mode detection, and aims to solve the problems of single-mode information loss and serious industrial field strong noise interference in the existing steel belt detection. The system synchronously integrates an eddy current sensor, a magnetic flux leakage sensor and an encoder through a multi-probe array adapter; and multi-dimensional damage physical information in the steel strip is obtained. An improved wavelet packet transform-empirical mode decomposition (WPT-EMD) collaborative noise reduction algorithm is adopted, and in combination with a sub-band energy entropy and a self-attention mechanism, non-stationary mechanical noise is effectively filtered out. A multi-rule feature extraction engine is used for extracting smooth residual errors, derivative mutation and other features, a support vector machine (DE-SVM) model introducing physical priori knowledge weights is constructed, and the small sample recognition problem is solved in combination with a virtual sample generation technology. The method can realize high-precision positioning and quantitative evaluation of steel strip damage under complex working conditions, and has the characteristics of strong anti-interference capability, high identification accuracy and good generalization performance.

The invention relates to the technical field of cervical vertebra rehabilitation systems, and discloses an intelligent self-adaptive cervical vertebra rehabilitation system. The system comprises a multi-mode sensing unit which synchronously collects form data and soft tissue data of the cervical vertebra of a user through an optical mark point array and an ultrasonic probe array to generate a multi-source sensing data set; the intelligent analysis unit constructs a cervical vertebra dynamic mechanical model by using a graph convolutional neural network based on the multi-source sensing data set, analyzes a cervical vertebra joint coupling relation and stress distribution, and generates an analysis parameter set; the strategy generation unit generates a personalized rehabilitation strategy according to the analysis parameter set, the personalized rehabilitation strategy comprises an action sequence and an impedance adjustment parameter, and a strategy instruction set is formed; the dynamic control unit responds to the strategy instruction set and combines muscle activity data fed back by the electromyographic signal sensor in real time to adjust an execution strategy and generate a control command set; and the flexible driving unit drives the soft robot outer skeleton to perform multi-axis linkage rehabilitation training based on the control command set.

The invention discloses a lithium battery full life cycle health state monitoring method, belongs to the technical field of lithium ion battery detection, and aims to identify pore defects and dynamic bubble behaviors in a battery in real time. According to the method, broadband ultrasonic waves are emitted through an air coupling multi-probe array, and back scattering signals caused by bubbles and uneven structures in a battery in the charging and discharging process are collected. And in combination with a porous medium acoustic theory, extracting time domain and frequency domain characteristic parameters related to the bubble form and distribution, and inputting the time domain and frequency domain characteristic parameters into a preset bubble state-signal response model. On the basis, the evolution state of the internal gas production defect of the battery is judged, so that effective support is provided for fault early warning and safe operation of the lithium ion battery. The method has the remarkable advantages of simplicity and convenience in operation, high real-time performance, high accuracy and the like, and can be widely applied to online detection and quality control of a battery production line.

The invention provides an inclined shaft gas-liquid slug flow ultrasonic flow velocity monitoring and calculating method and system, and the system comprises an ultrasonic flow velocity monitoring probe array module, a signal collection and preprocessing module, and a data processing and calculating module. The method comprises the following steps: performing variational mode decomposition on a signal, separating characteristic components of different flow structures in slug flow, realizing real-time discrimination of a liquid slug and a liquid film section and accurate capture of liquid phase apparent velocity, and accurately calculating gas phase apparent velocity through a unified mechanism model. Through the multi-angle same-direction array design, the measurement deviation caused by non-uniform cross section flow velocity distribution of the inclined shaft is overcome; the composite signal in the slug flow is effectively separated through variational mode decomposition, and the frequency shift extraction precision is improved; the innovative real-time flow state discrimination method based on the signal-to-noise ratio realizes the self-adaption of the measurement model to the flow structure, and significantly improves the measurement accuracy under the complex intermittent flow.

The invention relates to the technical field of aluminum ash recycling digital twinning, and discloses an aluminum ash recycling treatment method based on a high-aluminum material. According to the method, a digital twinborn body in a processing process is constructed, and a virtual probe array corresponding to a key reaction site of a real device is set in the digital twinborn body, so that dynamic evolution information of aluminum ash processing is continuously captured. An evolution chain of a physical field and a material flow is constructed based on the information, then a processing path is deduced reversely, and key evolution nodes and constraint conditions are identified. The nodes and conditions are converted into a control strategy set, and a real processing system is driven to execute closed-loop regulation and control. According to the method, deep perspective of the internal evolution mechanism in the aluminum ash treatment process is achieved, accurate regulation and control are achieved based on causal analysis, and the stability and the resource recovery rate of the treatment process are improved.

The invention provides a landslide early warning and evaluation method and system. The landslide early warning and evaluation method comprises the steps of uploading target parameters obtained through periodic measurement of a probe array to a cloud platform, dynamically inverting shear strength parameters of depth points of a sensing section based on the target parameters through an inversion model, and calculating a safety coefficient according to the shear strength parameters; wherein when the safety coefficient is larger than a set threshold value, pre-sliding early warning is triggered; and monitoring the temperature time sequence in real time to obtain the depth of the sliding surface and trigger landslide early warning when the temperature time sequence of any depth point meets the condition of the sliding event, evaluating the volume of the landslide mass based on the depth of the sliding surface, and outputting a landslide report. According to the invention, in-situ and continuous measurement of the target parameters of the soil body can be realized, the attenuation process of the shear strength of the soil body can be sensed in advance, and the loss possibly caused by landslide disasters is reduced; meanwhile, the scale and the influence range of the landslide can be quickly known after the landslide occurs.

The invention discloses an insulating pull rod full-automatic detection system and method based on an annular ultrasonic probe group. The system comprises a detection assembly; an insulating pull rod is arranged in the detection assembly, and the detection assembly is electrically connected with the signal processing unit, the defect analysis module and the computer; the detection assembly comprises an annular ultrasonic probe group and an automatic stepping device; the two ends of the annular ultrasonic probe set are connected with the automatic stepping device, and a stepping track is arranged in the automatic stepping device and used for driving the annular ultrasonic probe set to stably move in the axial direction of the insulation pull rod. Full-circle scanning of the insulation pull rod is achieved through the annular ultrasonic probe set, the problems of poor coupling and missing detection in traditional ultrasonic detection are effectively solved by matching with the spiral scanning path of the automatic stepping device and the self-adaptive adjusting mechanism, meanwhile, the position is calibrated in real time through the infrared distance measuring sensor, and it is ensured that the detection precision is stable.

This invention belongs to the field of concrete pouring technology, and particularly relates to a precise control method for pile foundation concrete pouring based on multi-source sensor fusion. Addressing the problems of large measurement errors, frequent over-pouring, and significant concrete waste in traditional pile foundation pouring, this invention employs a high-frequency ultrasonic probe array coaxially fixed to the outer wall of the pouring guide pipe to uniformly elevate and scan, acquiring three-dimensional point cloud data of the entire pile foundation. After two levels of outlier removal and adaptive multi-scale spectral filtering preprocessing, combined with the expansion of the over-pouring section point cloud, a high-precision three-dimensional model of the pile foundation is constructed. Cross-sectional features at each depth are extracted to generate a pumping flow rate feedforward compensation curve, and a segmented adaptive improved PID controller is constructed with real-time concrete interface elevation and rise speed as feedback quantities to dynamically regulate the pumping flow rate. This invention achieves automated, intelligent, and precise control of the entire pile foundation pouring process, effectively improving pouring control accuracy, significantly reducing concrete waste and construction costs, and solving the measurement and pouring control problems of traditional construction.

The application discloses a GIS insulator partial discharge charge on-line detection method and system, and relates to the technical field of charge detection.The method comprises the following steps: according to a GIS simulation model, simulating the electric field distribution of charge generation in the partial discharge process, determining the performance index of the required electric field sensing probe, and preparing the electric field sensing probe; according to an epoxy resin model, obtaining the charge density, the charge distribution range and the electric field signal collected by the electric field sensing probe under different voltage pulses, and constructing a test data set; according to the GIS simulation model, obtaining the electric field signal under different charge densities and different charge distribution radii, and constructing a training data set; training a neural network model according to the training data set, and correcting according to the test data set; and collecting the partial discharge electric field signal by using the electric field sensing probe array arranged on the outer wall of the GIS insulator, and obtaining the charge density and the charge distribution range on the surface of the insulator by using the neural network. Non-invasive and non-contact on-line measurement is realized.

A method and device for human behavior recognition in ecological protection zones based on gait feature recognition are proposed. The method involves laying a ring-shaped first optical fiber detection layer around the perimeter of the protected area's isolation zone; deploying a radial second optical fiber detection layer along the isolation zone towards the center of the protected area; and deploying a tri-band WiFi probe array within the isolation zone. An intrusion location recognition module uses vibration signals detected by the first optical fiber detection layer to calculate the intrusion location coordinates using a time difference positioning formula, and activates the monitoring channels of the corresponding radial second optical fiber detection layer based on the location coordinates. A gait feature fusion and extraction module extracts multi-scale energy features and generates comprehensive gait features. An identity discrimination module inputs the comprehensive gait features into a pre-trained classification model and determines the identity of the intruder based on the comprehensive gait features, thereby constructing an intelligent, dynamic, interconnected, and low-cost ecological protection technology system for ecological protection zones.

The invention discloses a bolt pre-tightening force monitoring system and method based on ultrasonic waves. The system comprises an ultrasonic probe, a data acquisition and processing analyzer and the like, a probe array is uniformly mounted on the surface of a bolt, an improved depth residual network is arranged in the data acquisition and processing analyzer and can process a flight time difference signal, and an audible and visual alarm can trigger a three-level alarm according to pre-tightening force deviation. The data remote transmission device provides data for the digital twin system, the display unit displays a pre-tightening force distribution atlas in real time, and a digital twin system platform generates a three-dimensional visual dynamic model and the like. According to the method, bolt pre-tightening force monitoring is achieved through the system. The system is high in measurement precision, convenient to install, capable of achieving real-time early warning and intelligent operation and maintenance, and suitable for various industrial fields.

The invention discloses a multi-field coupling intelligent inclination angle plane physical similar simulation experiment system and method. The system comprises an experiment platform with a controllable inclination angle, a stress monitoring module, an optical deformation monitoring module and an acoustic emission monitoring module. The experiment platform realizes high-precision adjustment and stable maintenance of an inclination angle of + / -0.1 degree through cooperation of hydraulic drive and a mechanical lock. The stress module adopts a wireless sensor network to monitor a stress field; the optical module is used for measuring full-field displacement strain based on a CCD camera and a digital image related technology; and the acoustic emission module positions a rock stratum fracture signal through the probe array. The method comprises the steps of platform deployment, inclination angle adjustment, system debugging, synchronous mining monitoring and the like. According to the invention, inclination angle accurate control and multi-physical field information synchronous integrated acquisition are realized, and the system is suitable for inclined coal seam mining rock stratum movement and disaster mechanism research.

The application provides a multi-probe array obstacle positioning method and device. The method comprises: in the case that multiple ultrasonic probes enter a transmitting mode of transmitting ultrasonic waves in turn and the multiple ultrasonic probes all enter a receiving mode of receiving ultrasonic waves, generating multi-dimensional time data corresponding to each transceiver pair; the transceiver pair comprises a pair of ultrasonic probes in the transmitting mode and an ultrasonic probe in the receiving mode; performing grid processing on the front area of the ultrasonic probe to obtain multiple grid points; determining a voting value corresponding to each grid point based on the multi-dimensional time data by using a spatial voting algorithm; and in the case that the voting value corresponding to the grid point is greater than a first threshold, determining positioning information of an obstacle based on the grid point. In this way, in a multi-obstacle scene, the positioning information of the obstacle can be confirmed by the voting values of the multiple grid points corresponding to the multiple grids in the front area of the ultrasonic probe, so that the purpose of accurate positioning and judgment in a multi-obstacle scene can be achieved.

The application discloses a pipeline inner wall welding seam self-adaptive monitoring device and method based on a multi-probe DCPD method, relates to the field of pipeline welding seam detection, and the self-adaptive monitoring device is configured as a monitoring robot which is adapted to walk along the inside of a pipeline and perform full-coverage direct-current potential drop monitoring on the inner wall welding seam. The monitoring robot comprises a walking support assembly, a monitoring driving assembly, a cleaning assembly and a main control unit. The walking support assembly is a pipeline inner wall self-adaptive support walking mechanism based on a triangular support, which comprises a moving driving module and two groups of support walking modules which are symmetrically arranged left and right. Each support walking module comprises a support arm, a mounting swing arm, a transmission assembly and a support pulley. Through the linkage mechanism of the walking support assembly and the synchronous radial sliding mechanism of the monitoring driving assembly, the self-adaptive walking of the inner wall of a pipeline with different diameters and the accurate close-contact detection of a multi-probe array are realized.

The invention relates to the technical field of industrial production traceability, in particular to an intelligent non-invasive workpiece traceability system based on an ultrasonic phased array technology, which comprises a data collection module, a feature extraction module and a traceability module, the interior of the workpiece is scanned at multiple angles, and full-matrix data are generated; the feature extraction module adopts a convolutional neural network to automatically extract internal structure and component distribution features, and generates a unique original traceability code; the traceability module associatively stores the data and the codes, re-extracts features in subsequent processing, matches the features with the database, and determines the identity of the workpiece; according to the system, the problems of surface treatment interference and internal information loss are solved, full-life-cycle traceability and quality monitoring are realized through noninvasive accurate acquisition, feature extraction and dynamic matching mechanisms, and the process reliability and the data security are improved.

The invention provides a water quality on-line analysis method and system in a radioactive waste liquid treatment process, and relates to the technical field of water quality on-line monitoring, and the method comprises the following steps: 1, introducing a clarified sample into an analysis pool, and collecting Raman spectrum characteristic peak data in real time through a plurality of groups of optical fiber Raman probe arrays arranged in the analysis pool along a waste liquid flowing direction; performing principal component analysis on the Raman spectrum characteristic peak data, extracting characteristic spectrum vectors corresponding to main radionuclides and interferents, and constructing a two-dimensional concentration distribution field according to continuous changes of the characteristic spectrum vectors in spatial positions; 2, isoconcentration lines are extracted from specific interferent components in the two-dimensional concentration distribution field, discrete sampling is carried out, the minimum circumcircle of all sampling points is calculated, and the circle center coordinates and the radius of the isoconcentration lines are obtained; the system realizes intelligent regulation and control of treatment process parameters, and ensures that the discharged water quality is stable and reaches the standard.

This invention discloses a comprehensive management method for transparent inspection of power grid materials based on the Internet of Things (IoT), belonging to the field of power material management technology. The method includes the following steps: deploying a three-axis vector probe array at the material entry channel to collect multi-point spatiotemporal signals and calculate the field strength distribution, generating an electromagnetic disturbance spatiotemporal matrix, and outputting a radio frequency identification (RFID) tag resonance drift risk matrix. This invention models electromagnetic disturbance sources using a three-axis vector probe array, combines resonance registration fingerprinting and frequency band compensation to achieve dynamic correction of RFID frequency drift, and integrates phase-encoded backscattering and strain acoustic features to ensure identification consistency. By utilizing optical time reference synchronization and phase-programmable metasurfaces to reconstruct the near-field environment, an adaptively adjustable identification space is constructed, improving the identification stability and traceability reliability of power grid materials under high-disturbance environments, thus breaking through the bottlenecks of traditional identification technologies.

The application provides a bubble behavior test device and method for simulating a liquid metal cooled reactor, and belongs to the technical field of nuclear engineering. The device aims to solve the problems of high operating temperature, large power consumption and poor safety of a traditional experimental loop using lead-bismuth alloy as a liquid phase medium, and the shortcoming that a traditional contact type measurement method easily causes disturbance to the flow characteristics of fluid. The bubble behavior test device for simulating the liquid metal cooled reactor comprises a flow channel body, a rod bundle matrix and liquid gallium-indium-tin alloy metal arranged in the flow channel body, and a plurality of gas check valves arranged on each rod of the rod bundle matrix; an inert gas output assembly is provided with a plurality of gas output ports and is connected with the gas injection ports on each rod of the rod bundle matrix; an ultrasonic probe array is arranged on the side of the rod bundle matrix and in the central rod; and a fluid circulation assembly is connected with the flow channel body at the inlet end and the outlet end and is used for driving the fluid in the flow channel body to move from top to bottom and separating the gas from the flow channel body. The device is mainly used for simulating the bubble behavior of the liquid metal cooled reactor.

The application provides an optical signal synchronous calibration device and method of an optical computing system, the device comprising: an optical probe array arranged on a chip, an AI coprocessor, an optical path tuning engine and a low-speed electrical feedback line arranged on a board card; the optical probe array detects optical signals of wavelength channels corresponding to a plurality of optical computing units and converts the optical signals into electrical signals; the AI coprocessor drives the corresponding optical computing unit to emit a detection pulse according to a pre-allocated time slot index variable, extracts a phase error from the electrical signal and calculates a final calibration quantity based on the phase error; the optical path tuning engine performs an optical path tuning operation based on the final calibration quantity to calibrate the error of the corresponding optical computing unit; and the low-speed electrical feedback line transmits the electrical signal to the AI coprocessor and transmits the detection pulse to the chip. The application realizes real-time calibration in a dynamic environment through spatial distributed detection and board-level centralized processing cooperation.

The present application relates to the technical field of biomedical sensing, and discloses a tumor microenvironment multi-parameter monitoring method, device, medium and equipment, the method comprising applying an excitation magnetic field to a magnetic nano-probe in a target region, using a photomagnetic probe array to collect a nonlinear magnetization response signal; performing harmonic decomposition and feature extraction on the nonlinear magnetization response signal to obtain a harmonic feature vector for characterizing the nonlinear magnetization response; inputting the harmonic feature vector into a physical constraint inversion engine to solve, simultaneously decouple and output quantitative estimation values of at least two tumor microenvironment parameters of the target region, the at least two tumor microenvironment parameters being any two or more of a pH value, an oxygen partial pressure, a local temperature and a magnetic nano-probe concentration; the physical constraint inversion engine is constructed based on a Bayesian inference framework fused with a physical constraint regularization term. The above method realizes simultaneous, high-precision, in-situ decoupling and monitoring of multiple parameters in a tumor microenvironment.

This invention discloses a method for classifying defects in patternless wafers based on multi-feature joint decision-making, belonging to the field of wafer defect detection technology. The method includes the following steps: irradiating the surface of a patternless wafer with a probe beam having a preset polarization state; acquiring spatial scattered light intensity information of the scattered light from the wafer surface under different combinations of polarization states based on a probe array distributed at multiple spatial angles; extracting local optical features of the candidate defect regions and constructing a feature tensor based on these local optical features; analyzing the candidate defect regions according to the polarization degradation evolution law of the feature tensor in the spectral dimension and outputting the defect classification result. This approach avoids the low classification accuracy caused by highly similar scattering features of small defects, significantly improving the accuracy of the detection results.

The application relates to the technical field of magnetic detection, in particular to a method and a test system for coupling a magnetic gradiometer and a SQUID to improve sensitivity, which comprises the following steps: deploying at least one set of magnetic gradiometer probe arrays in a superconducting shielding environment to obtain original magnetic gradiometer tensor data streams of a target area; feeding a multi-channel SQUID front-end input coil through a superconducting transmission line; completing magnetic flux locking and amplification to generate a preliminary signal by the SQUID; suppressing environmental and SQUID background noise by using an adaptive digital noise reduction processor to obtain a high-fidelity magnetic gradiometer signal; and generating a high-resolution magnetic gradiometer distribution map through gradient reconstruction and spatial resolution enhancement. The superconducting shielding environment combines array acquisition to suppress external interference and retain complete tensor characteristics, and the superconducting transmission line combines a multi-channel SQUID to reduce transmission noise and cumulative error, so that the signal is closer to the original state, and the SQUID locking sensitivity and amplification fidelity are improved. The application improves the sensitivity of magnetic gradiometer detection and the accuracy of target identification.

The invention discloses a high-precision linear palladium alloy wire and a preparation method thereof and a high-precision probe, and the palladium alloy wire comprises the following components in percentage by weight: 38-43% of Pd, 25-35% of Cu, 20-32% of Ag, 0.01-2% of trace elements, and at least one selected from Zn, Ni, B, Al and In, the palladium alloy wire is obtained by carrying out at least three stages of fine drawing through a drawing assembly with the surface roughness Ra being smaller than 0.05 micrometer, the diameter is smaller than 0.8 mm, the surface roughness Ra is smaller than 0.05 micrometer, the diameter tolerance is within + / -0.002 mm, and the roundness is within 0.001 mm. According to the method, the palladium alloy wire material is comprehensively optimized in component design and processing path, so that the palladium alloy wire material still has relatively small diameter tolerance, good roundness and relatively low surface roughness under the condition of a micro diameter, and the quality uniformity and the test stability and precision of a probe array are favorably improved.

The present application belongs to the field of semiconductor manufacturing monocrystalline silicon, and particularly relates to a semiconductor wafer surface treatment method based on selective intervention window. In view of the problem that the traditional process cannot simultaneously consider global uniform treatment and local problem repair, an intelligent closed-loop solution is proposed. The method comprises the following steps: firstly, an in-situ characterization system integrated with a micro-probe array is constructed to collect and decouple multi-dimensional state fingerprint maps reflecting the electrochemical activity and thermal diffusion characteristics of the wafer surface in real time; then, a neural network algorithm optimized by transfer learning is used to identify abnormal areas causing product failure, and the abnormal areas are defined as a dynamic and adaptive selective intervention window; a micro-electro-mechanical system hydraulic field array is driven to realize precise cooperation of ultrasonic micro-polishing contacts, multi-channel micro-nozzles and negative pressure adsorption micro-holes. The present application decouples macro-background treatment and micro-differentiated intervention, improves the processing yield, enhances the process robustness and reduces the comprehensive operation cost.

The embodiment of the invention provides an unstructured data processing system based on artificial intelligence, and the unstructured data processing system based on artificial intelligence comprises multi-modal original data obtained through a distributed probe array which comprises a text scanning unit, an image perception unit and an audio and video synchronization unit; the characteristic smelting center is connected with the probe array and is provided with an anti-decoupling module and a cross-modal attention grafting module; the evolutionary engine is dynamically connected with the smelting center and comprises a periodic self-checking neural architecture searcher and a knowledge incubator based on analogy reasoning; and the system outputs a 128-dimensional composite feature code with a timestamp. Through the cross-modal fusion technology of the feature melting center, the semantic understanding depth which cannot be achieved by a traditional single-modal system is achieved, a dynamic evolution engine ensures that the processing precision is continuously optimized along with time, and the common industry problem of model performance degradation is overcome.

The application provides an evaluation device and method for phase measurement error of a spherical near-field antenna when the antenna is installed eccentrically, which can evaluate the phase measurement error of a to-be-measured antenna in a certain direction, a certain section and a certain angle range of a space under different eccentric distances of the antenna in a field. The device comprises a spherical near-field scanning ring 7, a low-frequency probe array 8, a high-frequency probe array 9, a spherical near-field test azimuth turntable 11 and an adapter tool 12. The two half rings of the spherical near-field scanning ring 7 are respectively arranged with the low-frequency probe array 8 and the high-frequency probe array 9. The spherical near-field test azimuth turntable 11 is located on the central axis of the spherical near-field scanning ring. The azimuth turntable 11 is connected with the adapter tool 12. The tool main body structure 1 and the counterweight 6 are connected with the turntable flange 3 and are installed on the adapter tool 12. The to-be-measured antenna 10 is installed on the antenna installation hole 2 of the test tool.

The application discloses a voltage testing method and device, which is used for detecting voltage values of a plurality of battery cells in a battery pack. The method comprises the following steps: determining the position of a measuring point corresponding to each battery cell in the battery pack, and controlling the battery pack to move so that each battery cell moves to a corresponding measuring area; determining the target position of each probe of a probe array according to the position of the measuring point corresponding to each battery cell; the number of probes in the probe array is greater than or equal to the number of battery cells in the battery pack; controlling a plurality of probes of the probe array to move to the target positions respectively; and controlling the entire probe array to move so that each probe in the probe array contacts the measuring point of the battery cell, thereby obtaining the voltage values of all battery cells in the battery pack. The application can be adapted to different specifications of battery packs, the target position of each probe of the probe array is determined according to the position of the measuring point corresponding to each battery cell, and then the probes are controlled to move towards the battery pack, so that all probes can reliably contact the measuring points of the battery pack at the same time.