375results about How to "Reduce computation" patented technology

The invention belongs to the technical field of image processing, and discloses a transformer fault detection method based on an improved YOLOv8 model. Aiming at the problems of inaccuracy in feature extraction, low precision, poor real-time detection capability and the like existing in fault detection of a traditional transformer fault detection method, the invention establishes a transformer fault detection model based on an improved YOLOV8 model architecture, a self-calibration convolution defogging module is introduced, a generative adversarial network (GAN) and a multi-source data fusion network structure are added, a BiFPN structure is used, an ASPP module is replaced by an ASPFCSPC module, and a YOLOV8 model architecture is improved, so that the feature extraction capability of a transformer fault detection model is enhanced, the calculation complexity of the model is reduced, and the fault detection efficiency of the transformer is improved. The transformer fault detection precision, accuracy and real-time detection capability are improved, and the stability and adaptability of the model are enhanced.

The invention discloses a current inversion method based on a magnetic sensor array, and the method comprises the steps: S1, setting a plurality of measurement points, uniformly dividing a conductor layout region in a to-be-measured part of a to-be-measured multi-conductor system into grids with M * N nodes, and numbering each grid node as (i, j); constructing a coordinate system and recording coordinates of all grid nodes; s2, establishing a finite element simulation calculation model, calculating a reference position coefficient matrix of all measurement points, and constructing a reference position coefficient matrix database; s3, calculating a conductor position coefficient matrix, and constructing a position coefficient matrix; s4, according to the magnetic field measurement value acquired by the magnetic sensor array, the conductor current is calculated through inversion; and S5, after resampling the magnetic field measurement value, substituting the updated measurement value and the position coefficient matrix stored in the step S4 into the linear equation set constructed in the step S4 to solve the current of each conductor. The method has the characteristics of rapid calculation and low operand while the accuracy is ensured, and the inversion calculation efficiency can be improved.

The invention provides a target positioning method and device based on a medical perspective. The method is applied to medical image processing. The method comprises the following steps: selecting a two-dimensional medical perspective image under any two visual angles, and obtaining two-dimensional pixel coordinates and image parameters of a target point on the perspective image; a target coordinate conversion matrix is calculated according to the image parameters, the target coordinate conversion matrix is used for converting two-dimensional pixel coordinates of the target point into three-dimensional world coordinates, and the target coordinate conversion matrix is composed of conversion parameters of a two-dimensional pixel coordinate system, a two-dimensional image coordinate system, a three-dimensional camera coordinate system and a three-dimensional world coordinate system; substituting the two-dimensional pixel coordinates of the target points of the double viewing angles into the target coordinate conversion matrix, and calculating to obtain the three-dimensional world coordinates of the initial target points of the double viewing angles; and processing and calculating according to the dual-view initial target point and the three-dimensional world coordinate to obtain the three-dimensional world coordinate of the target point. According to the invention, the target point can be quickly and accurately positioned.

The invention discloses a power distribution network risk assessment method based on a photovoltaic output prediction error, and the method comprises the steps: constructing an isolated tree of an isolated Sessen algorithm, carrying out the fusion processing of the historical data of mass distributed photovoltaic output, and correcting the abnormal value of the photovoltaic output; a probability distribution model of a photovoltaic output prediction error is constructed based on a photovoltaic output typical scene, a power distribution network photovoltaic typical output scene is divided based on an adaptive K-means clustering algorithm, and distributed photovoltaic output is subjected to ultra-short-term prediction by adopting BiLSTM according to meteorological conditions to obtain a photovoltaic output prediction curve. Carrying out modeling on the photovoltaic output prediction error by adopting KDE to obtain a probability distribution curve of the photovoltaic output prediction error; in combination with a semi-invariant method and a Gram-Charger expansion series method, probability distribution of voltage of each node of the power distribution network is obtained through probabilistic load flow calculation; and calculating the node voltage out-of-limit probability of the power distribution network, and evaluating the voltage out-of-limit risk of the power distribution network. According to the method, the key uncertainty factors in the change of the running state of the power distribution network can be determined.

The invention discloses a power grid charging strategy determination method and device and electronic equipment. The method comprises the steps that a dynamic space charging model of a predetermined charging station is established, and the dynamic space charging model comprises a plurality of data items; according to the dynamic space charging model, an objective function and constraint conditions used for determining an electric vehicle charging strategy are constructed, the objective function aims at minimizing the charging cost of an electric vehicle user and maximizing the charging amount of the electric vehicle, and the constraint conditions comprise the battery capacity constraint and the battery remaining capacity constraint; the demand constraint of a user on the SOC of the battery and the charging electricity price constraint during parking are carried out; and solving the objective function under the constraint condition to obtain a power grid charging strategy. The technical problem that the determined power grid charging strategy is not accurate when the power grid charging strategy for balancing the charging cost and the charging electric quantity is determined in the prior art is solved.

The invention relates to a power data federated learning privacy protection method based on efficient homomorphic encryption. The method comprises the following steps: step 1, a client initializes a neural network model; step 2, the server side encrypts and updates local neural network model gradient parameters uploaded by the client side; 3, the server side executes gradient average aggregation operation, the server side does not need to decrypt, the encrypted gradient parameters uploaded by the client side are directly calculated, an average value is solved, and therefore global gradient parameters are updated; step 4, after each client receives the updated global gradient, carrying out decryption calculation operation; 5, loading the updated weight parameters of the neural network model into a local neural network model, and performing neural network model training by using a local data set; the problems that an existing encryption technology is large in operand, large in computing resource overhead and low in algorithm efficiency, and privacy leakage exists in the transmission and analysis process of power data can be solved.

The invention discloses a container corner fitting positioning method, device and equipment and a storage medium, a target area on a container is shot through a monocular camera and a three-dimensional structured light camera, an RGB image and a depth image corresponding to the target area are obtained, and the target area comprises corner fittings; performing target detection processing on the RGB image to obtain a side hole detection frame corresponding to a side hole in the corner fitting; determining a target image area corresponding to the side hole detection frame in the depth image; converting the target image area into a first three-dimensional point cloud; extracting edge point clouds of the side holes from the first three-dimensional point clouds; determining first attitude information of the side hole relative to the three-dimensional structured light camera based on the edge point cloud; and determining second attitude information of the lockhole in the corner piece relative to the three-dimensional structured light camera based on the first attitude information. According to the embodiment of the invention, the problems of low robustness, weak real-time performance, large scene dependence and high construction cost existing in current corner fitting positioning can be solved.

The invention discloses a control method and a control system of an ultrasonic therapeutic apparatus and the ultrasonic therapeutic apparatus, and the method comprises the following steps: S1, transmitting ultrasonic waves to skins at a plurality of target positions one by one for sampling, and receiving an ultrasonic echo signal of each target position, the plurality of target positions are obtained by a transducer of the ultrasonic therapeutic apparatus moving along a skin surface area directly facing the end face of a probe of the ultrasonic therapeutic apparatus; s2, converting the received ultrasonic echo signal into an ultrasonic image; s3, skin features of each target position in the ultrasonic image are extracted, the distance between the skin of each target position and the end face of the probe is calculated, the calculated maximum distance is compared with a target threshold value, and if the maximum distance is smaller than or equal to the target threshold value, it is judged that the end face of the probe is effectively attached to the surface of the skin; otherwise, judging that the end face of the probe is not effectively attached to the skin surface. Positioning is carried out through ultrasonic imaging, it is ensured that operation is carried out in the state that the end face of the probe is effectively attached to the skin surface area, and the using effect and safety are ensured.

The invention belongs to the technical field of automatic control and inertial navigation. The invention provides a geographic tracking rapid correction method for an airborne photoelectric pod. The method comprises the following steps: after the photoelectric pod receives a geographical tracking task instruction of a ground control center, calculating an azimuth angle and a pitch angle of the photoelectric pod required for aligning an aiming line of the photoelectric pod to a target prediction point by combining information of longitude and latitude heights of a carrier aircraft, longitude and latitude heights of a target point, an aircraft attitude angle and a frame angle of the photoelectric pod; and then a geographical tracking PID controller is used for controlling and turning, meanwhile, a ground image returned in real time is observed, and if the aiming line of the photoelectric pod deviates from the position of the target point, a direction key can be used for compensation and correction until the position of the target point coincides with the center of the image. According to the embodiment of the invention, the method can achieve the precise locking of a target through the quick correction of the geographic tracking error of the airborne photoelectric pod. And moreover, the calculation amount is small, the precision is high, the instantaneity is strong, and the task completion efficiency and success rate are improved.

The invention provides a guardrail region critical dimension detection method, which comprises a three-dimensional point cloud acquisition step, a profile positioning and extraction step and a width value acquisition step, and in the method, a three-dimensional point cloud of a guardrail region is generated through profile data acquired by a high-precision line laser sensor and corresponding mileage data, and then the three-dimensional point cloud is converted into a two-dimensional image; based on the two-dimensional image and the structural features of the guardrail, the starting point and the ending point of the guardrail are recognized, the mileage values of the guardrail are obtained, and then the mileage values of the preset detection positions can be obtained according to the mileage values of the starting point and the ending point of the guardrail and the preset standard distances between the starting point and the ending point of the guardrail and the preset detection positions. Therefore, the contour data of the predetermined detection position is extracted, and high-precision automatic detection of multiple key dimensions of the guardrail area is realized. According to the method, the two-dimensional image is used as an index to carry out positioning and contour data frame extraction, positioning is accurate, the calculation amount is small, and a result can be rapidly obtained on a track detection site.

The invention discloses a three-dimensional model package arranging method, device and equipment, a medium and a product, and relates to the technical field of three-dimensional (3D) printing.The method comprises the steps that space parameters of a to-be-printed model and a printing area are determined, the to-be-printed model comprises i three-dimensional models, and i is a positive integer larger than 1; based on the model parameters of the i three-dimensional models, the arrangement sequence of the i three-dimensional models is determined, and the model parameters are the contour parameters of the minimum enclosing rectangles of the three-dimensional models; and the i three-dimensional models are placed in the printing area based on the space parameters of the printing area and the placing sequence of the i three-dimensional models by utilizing a preset model printing rule. According to the method, the arrangement uniformity of the printing models can be improved while the package arrangement speed is ensured, the to-be-printed models are arranged in the printing area as many as possible, excessive analysis on the to-be-printed models is avoided, the calculation amount of a processor is saved, package arrangement efficiency and package arrangement quality are both considered, and the package arrangement experience of a user is improved.

The invention discloses a multi-satellite autonomous cooperation method for wide-area searching and tracking of a maritime moving target, and the method comprises the steps: S1, carrying out the preliminary observation of a set maritime target region through a high-orbit satellite, obtaining rough target information, and transmitting the rough target information to a ground station in real time; s2, the ground station forms an initial marine target situation list; s3, the ground station generates a high and low orbit satellite observation demand list according to the marine target situation list; s4, the ground station plans a task sequence of the satellite at the current moment according to the high and low orbit satellite observation demand information and sends the task sequence to the corresponding satellite, and the corresponding satellite executes the task sequence and feeds back an observation result to the ground station; and S5, the ground station updates the sea target situation list according to the observation results of the corresponding satellites, and the step S3 is returned to realize wide-area search and continuous tracking of the sea target. According to the invention, situation updating and prediction can be carried out according to dynamic information, dynamic observation requirements are automatically generated, and a satellite can rapidly plan task response requirements.

The invention discloses an aircraft noise source positioning method comprising the following steps: sound field signals are collected, and the sound field signals comprise sound pressure signals and three-dimensional particle vibration velocity signals; preprocessing the sound pressure signal and the three-dimensional particle vibration velocity signal to obtain frequency spectrum data; inputting the frequency spectrum data into a trained noise recognition model, and judging a noise event; when the noise event is judged to be aircraft noise, noise source positioning is carried out according to corresponding spectrum data; according to the invention, noise event identification can be carried out firstly, so that the influence of the sound source arrival direction and other noise sources is eliminated, and errors are avoided; and the sound pressure and the three-dimensional particle vibration velocity are collected for preprocessing, effective sound intensity signals are obtained for positioning, and the positioning precision is improved.

The invention belongs to the technical field of image processing, and provides a small-target lightweight substation equipment defect detection method and system, and the method comprises the steps: obtaining a substation equipment image; according to the acquired image and a preset small target lightweight defect detection model, completing defect detection of the substation equipment; wherein the preset small target lightweight defect detection model adopts a YOLOv8 target detection network which adopts an Officient Former V2 and a separation convolution attention mechanism as a backbone network, adopts scale time sequence feature fusion as a neck network and adopts a prediction head network of a shared convolution lightweight detection head, and image features are extracted based on the backbone network; obtaining feature maps of different scales; the neck network performs feature fusion on the feature maps with different scales to obtain a multi-scale feature map; and detecting the multi-scale feature map through the shared convolutional lightweight detection head, and performing defect positioning of the equipment image to complete defect detection of the substation equipment.

The invention discloses a quadruped robot obstacle avoidance method and system based on cooperation of a laser radar and a depth camera, and relates to the technical field of robot obstacle avoidance. The system comprises a quadruped robot, and the quadruped robot is provided with an industrial personal computer, a laser radar, a laser radar protection device and a depth camera. When the robot walks, the laser radar obstacle avoidance method is executed firstly, when a laser radar obstacle avoidance program fails due to the conditions of large blind areas and the like, the robot continues to walk until the laser radar obstacle avoidance program appears in the detection range of the depth camera, the depth camera obstacle avoidance method is executed for obstacle avoidance judgment again, and selection is made. The laser radar and the depth camera are combined for use to realize obstacle avoidance, and the depth camera is used as a blind compensation means and supplementation of laser obstacle avoidance, so that the safety is further improved. Processing depth data acquired by the depth camera by using a random forest; the operation amount is small and the processing speed is high.

The invention provides a hybrid anchor lane line detection result fusion method based on an attention mechanism, and the method comprises the steps: carrying out the feature extraction of an original image, and obtaining a feature map containing high-level semantics; inputting the global feature map into two multi-layer perceptron in parallel for processing, and outputting a data structure shape matched with the row anchor definition and the column anchor definition; post-processing is carried out, and line anchor and column anchor identification results of lane line coordinates are obtained through conversion; taking identification results of the row anchor and the column anchor as input, integrating information of the row anchor and the column anchor through an attention mechanism, distributing different confidence coefficients for the row anchor and the column anchor, and calculating an offset value of an output lane line coordinate; carrying out normalization processing on the confidence coefficient and the deviation value; and fusing lane line detection results of the row anchor and the column anchor to obtain an identification result. The method can adapt to different road environments and weather conditions, has high real-time performance and good recognition precision, and has remarkable advantages in the aspect of improving the accuracy and robustness of lane line detection.

The invention provides a vocoder speech synthesis processing method and device based on low resources and related equipment, and aims to solve the problem of low speech synthesis efficiency of a vocoder under the condition of low resources in the prior art. The method comprises the following steps: acquiring T frames of acoustic features, determining first target features corresponding to features including but not limited to vertical correlation between the T frames of acoustic features, performing convolution on the first target features based on respective corresponding channels, and determining features including but not limited to correlation between different channels of the acoustic features included in the first target features to obtain second target features; and determining a sampling point corresponding to the acoustic feature of each frame of voice signal according to the second target feature to obtain a target sampling point, and finally reconstructing the target sampling point into a voice waveform to obtain a target voice, so that the voice synthesis effect can be ensured, the voice synthesis efficiency of the vocoder under the condition of low resources can be improved, and the user experience can be improved. The technical scheme can be applied to the technical field of financial science and technology.

The invention discloses a two-degree-of-freedom permanent magnet synchronous motor driving method and a driving device thereof. A current instruction value of a d-q axis passes through a first coordinate conversion module to obtain an instruction value of a three-phase stator current; then comparing with a three-phase current actual value fed back by sampling, and obtaining a feedback item of a three-phase instruction voltage through a PI regulator module; deriving a recursive relational expression of d-q axis current along with the change of the control period, processing three-phase voltage fed back by the motor through a third coordinate transformation module to obtain d-q axis voltage, substituting the feedback three-phase current through a second coordinate transformation module to obtain d-q axis current and a d-q axis current instruction value into the recursive relational expression, and finally obtaining a three-phase instruction voltage feed-forward item; and adding the feed-forward item and the feedback item to obtain a three-phase voltage instruction value, inputting the three-phase voltage instruction value into a switch function module, determining a power device in the controlled three-level inverter bridge module according to the positive and negative of each instruction value, comparing the power device with a carrier wave to obtain a required PWM wave, and finally obtaining an alternating current and outputting the alternating current to the three-phase permanent magnet synchronous motor.

The invention discloses a multi-target detection method and system based on a millimeter wave radar, and the method comprises the steps: obtaining original echo data collected by the millimeter wave radar, and the original echo data comprise target data points of a plurality of targets; selecting a preset number of data points from the target data points of the plurality of targets, and taking the selected data points as effective data points; and detecting the plurality of targets based on the valid data points. The non-strict threshold value screening constant false alarm rate algorithm is combined with the dual index determination method and the spatial distance judgment algorithm, so that the influence of the multipath effect on result data can be reduced while multi-target detection is realized, the accuracy of target point detection is improved, the output data volume of frequency domain data processing is greatly reduced, and the accuracy of target point detection is improved. The operation amount of logic processing is reduced, and implementation of the algorithm on edge end mobile equipment is facilitated.

The method comprises the steps of rail piece contour obtaining, rail piece area positioning, rail head contour external rectangle obtaining and frame size calculation, according to contour data collected by a high-precision line laser sensor and corresponding mileage data, all target detection positions are automatically positioned, and corresponding contour data are extracted; according to the relative position distribution of the rail piece at the target detection position, the stock rail head area and the stock rail head and switch rail head area of the switch rail section are automatically identified and positioned from the extracted contour data, the size of the turnout frame is calculated through the minimum enclosing rectangle, and the method is convenient to operate, high in detection precision and high in accuracy. The method can be applied to various tracks with different sizes, only one frame of contour at the preset detection position is extracted for subsequent calculation and analysis, three-dimensional modeling, three-dimensional model comparison and other operations are not needed according to contour data, the operand is very small, and a detection result can be quickly obtained on a track detection site.

The invention provides a track checking interval and back protection distance detection method, which comprises a positioning extraction step, a contour combination step, a track piece identification step, a checking interval acquisition step and a back protection distance acquisition step, according to the method, a predetermined detection position is automatically positioned through profile data collected by a high-precision line laser sensor and corresponding mileage data, profile data of the position are extracted, then the profile data of the multiple line laser sensors are combined, profiles of the heads of the multiple rail pieces are automatically recognized and extracted from combined profile lines, and the accuracy of detection is improved. Due to the fact that the arrangement of the rail pieces at the preset detection position is fixed, the profiles of the guard rail head, the guard rail pair side wing rail head and the point rail can be conveniently and rapidly obtained, then the accurate checking interval value and the back protection distance value are rapidly obtained based on profile analysis and calculation, operation is convenient, detection precision is high, the calculation amount is small, and the detection accuracy is high. And a detection result can be quickly obtained on a track detection site.

The invention discloses an arbitrary-scale image super-resolution reconstruction method based on local features. According to the invention, two efficient plug-and-play modules are designed and can be combined with an existing fixed-scale super-resolution network so as to realize a super-resolution task of any scale. Wherein the scale perception feature adaptive module (SLFAM) solves the problem of lack of adaptability to the scale and the local structure of an image in the feature extraction process by generating a scale perception feature adaptive dynamic convolution kernel; the local feature adaptive up-sampling module (LFAUM) fuses scale information, projection position coordinates, local features, receptive field offset and other information in the up-sampling reconstruction process to generate a deformable up-sampling convolution kernel, and the function of up-sampling reconstruction of any scale is achieved. The method is mainly applied to the technical field of image super-resolution reconstruction, and has wide application prospects in the fields of remote sensing satellite images, medical images, security monitoring and the like.

The invention belongs to the technical field of industrial defect detection, and particularly relates to a PCB defect detection method based on improved YOLOv8, which comprises the following steps: 1) establishing a YOLOv8 model, improving an extraction part, a C2f module and a PAN-FPN module in the YOLOv8 model, and designing an input end of the YOLOv8 model to realize data enhancement of an image; and finally, an improved YOLOv8 model is constructed. 2) based on the YOLOv8 model improved in the step 1), inputting the acquired images of various PCB defect types into the input end of the improved YOLOv8 model through an industrial camera for data set training; in the training process, data enhancement is realized to promote the model to better utilize the correlation between different samples, so that the generalization ability and accuracy of the model are improved; and 3) in combination with a neural network structure for realizing different scale feature fusion in the improved YOLOv8 model, improving the detection capability of defects of different sizes and shapes of the PCB, outputting identified and positioned PCB defect areas, and generating a corresponding detection report.

The invention provides a target translation compensation and three-dimensional imaging method and system for a close-range millimeter wave MIMO radar, and the method and system achieve the high-quality real-time three-dimensional imaging. The method comprises the following steps: receiving echo data and movement speed of a tested person, and carrying out phase calibration from multi-station sampling to single-station sampling on the echo data; performing two-dimensional Fourier transform and motion compensation in x and y directions on the echo data after phase calibration based on the motion speed of the tested person; performing two-dimensional inverse Fourier transform on the echo data after two-dimensional Fourier transform and operation compensation in the wave number domain # imgabs0 # and # imgabs1 # directions; performing coherent superposition on the echo data after the two-dimensional inverse Fourier transform to obtain echo data after motion compensation of the tested person; based on a static target three-dimensional imaging algorithm principle, processing the echo data after motion compensation of the tested person to obtain two-dimensional images of each distance direction plane; and performing traversal synthesis processing on the two-dimensional image in each distance direction plane to obtain a three-dimensional image of the tested person.

The invention discloses a data processing device and method, a medium and computing equipment, and relates to the field of data processing. The device comprises a first state machine, a second state machine, a third state machine, a fourth state machine and a multiplier-adder. The first state machine is used for determining a storage address and a cumulative vector address of each data sample contained in the data packet to be subjected to dimension reduction in the memory module and sending a plurality of reading requests to the memory module; the second state machine is used for receiving original vector data returned by each reading request and sending the original vector data to the multiplier-adder; the third state machine is used for receiving accumulated vector data returned by each reading request and sending the accumulated vector data to the multiplier-adder; the multiplier-adder is used for accumulating the original vector data and the accumulated vector data in the same reading request and sending an accumulation result to the fourth state machine; and the fourth state machine is used for updating the accumulation result to the memory module. The four state machines and the multipliers work in parallel, so that the data throughput and the data processing speed are improved.

The invention relates to a data processing system for determining similar users, which comprises a processor and a memory in which a computer program is stored, and when the computer program is executed by the processor, the following steps are realized: obtaining an initial feature value list set corresponding to an initial user ID list, obtaining an initial feature vector list set corresponding to the initial user ID list, and obtaining a user ID list set corresponding to the initial feature vector list set; obtaining a candidate feature vector list set corresponding to the initial user ID list, obtaining a specified feature vector list set corresponding to the initial user ID list, obtaining a target similarity list corresponding to the initial user ID list according to the specified feature vector list set, determining similar users of the target user from the initial user ID list, and sending the similar users to the target user. According to the method, meaningless data are deleted in the process of obtaining the similar users, meanwhile, data with small influence on the similarity is deleted, the calculation amount of follow-up similarity calculation is reduced, the efficiency of obtaining the similar users is improved, and the accuracy of obtaining the similar users is improved.

The invention belongs to the technical field of cutting machining, and relates to a cutting simulation blank surface reconstruction method and device and a readable storage medium. Discretizing the tool and the blank to obtain a tool Tri-Dexel model and a blank Tri-Dexel model, and extracting a tool Dexel chain set and a blank Dexel chain set; boolean operation is conducted on the tool Dexcel chain and the blank Dexcel chain, a blank surface voxel model and the cutting condition of the tool Dexcel on all edges of blank surface voxels are obtained, then the state value of all angular points of the blank surface voxels is obtained, and whether the voxels are boundary voxels containing contour surfaces or not is judged; taking the voxel containing the contour surface as a to-be-cut voxel, obtaining a triangular mesh formed by intersection points of the to-be-cut voxel and the contour surface, and calculating vertex coordinates and a normal vector of the triangular mesh; and obtaining a surface grid based on the vertex coordinates and the normal vectors of all the triangular grids so as to perform surface reconstruction on the blank.

The invention discloses a three-dimensional attribute detection method and device and a storage medium. The method comprises the steps that a sensor located at the top of a lift car is called in an elevator to detect and sense image data downwards; sensing that a pixel point in the image data has a depth value; performing self-correction on the depth values of the pixel points in the perception image data; if the self-correction is completed, detecting image data of a region where the target object is located in the sensing image data according to the lift car; and under the condition of filtering out the background of the lift car, carrying out statistics on the three-dimensional attribute of the target object in the regional image data. According to the embodiment, the data size is greatly reduced through operations such as target detection and background filtering, so that the calculation amount is effectively reduced, the method is suitable for hardware with low elevator calculation power, and the calculation efficiency is improved. Moreover, interference is gradually eliminated through operations such as target detection and background filtering, the target object is gradually converged, the three-dimensional attributes are counted for the target object, and the accuracy of the three-dimensional attributes can be improved.

The invention discloses a high-precision automatic identification and classification method for styles and features along a street based on a three-dimensional topology network. The method comprises the steps of current street scene image data acquisition, three-dimensional form point cloud data preprocessing, three-dimensional topology network construction, street type identification, type identification result verification and man-machine interaction display feedback. According to the method, street three-dimensional space forms are automatically identified and classified by using point cloud data of the street three-dimensional space forms and adopting a graph neural network technology. According to the method, street space type identification in the urban planning field can be coped with, a street three-dimensional space form type database based on the graph neural network technology is established, automatic identification and classification of street three-dimensional space form types are realized, the accuracy of urban street three-dimensional space identification and classification is improved in an accurate quantification mode, and the method is suitable for popularization and application. And a powerful support and a reliable basis are provided for refined management and control of the urban street space three-dimensional form.

The invention provides a solid state disk garbage collection and host write address repetition detection method, which comprises the following steps that: a host covers data written into a logic address, finds a first physical address which is written in a covered manner, and writes the first physical address into a hardware acceleration module; reading continuous physical address data in batches at the beginning of garbage collection, and writing a currently read second physical address into a hardware acceleration module; the hardware acceleration module compares the first physical address with the second physical address; after data are read through garbage collection, a comparison result of the hardware acceleration module is obtained, if the comparison result is consistent, the current second physical address is skipped, continuous physical address data are continuously read, and if the comparison result is inconsistent, the second physical address data are written into a new physical address; and the steps are repeated until garbage recycling is completed. According to the invention, the speed of garbage collection and data writing is accelerated, so that the purposes of improving the data transmission efficiency and reducing the use power consumption are achieved.