60results about How to "Avoid redundant calculations" patented technology

The invention relates to the technical field of mechanical fault diagnosis, in particular to a mechanical fault diagnosis method and system based on a pulse neural network. The method comprises the following steps: constructing a fault diagnosis network, wherein the fault diagnosis network comprises an improved gating attention coding module and a pulse residual network; the method comprises the following steps: replacing a common convolution layer of a gating attention coding module with a one-dimensional sparse convolution layer, and replacing a two-dimensional batch normalization layer with a one-dimensional batch normalization layer to obtain an improved gating attention coding module; setting a pulse neuron in the fault diagnosis network as a PSN neuron, and embedding a time step shrinkage layer in the pulse residual network; and inputting the mechanical vibration signal of the current sampling period into a fault diagnosis network, and outputting a predicted fault category label of the current sampling period. According to the invention, high-efficiency and high-precision mechanical fault intelligent diagnosis is realized.

The invention discloses a chip built-in self-test memory grouping method based on a BPPC algorithm, and belongs to the technical field of calculation, reckoning or counting. The method comprises the following steps: extracting memory file information, preprocessing, and storing in a memory information structure array; through a K-means + + algorithm, performing preliminary clustering on the memories according to the attributes of the memories; constructing a constraint relationship between the memories through distance constraint, and representing the constraint relationship by using a conflict matrix; a compatible memory group is decomposed into memory groups without a distance relation by using a vertex coloring problem model, the memory groups without a constraint relation are subjected to one-dimensional boxing by using an FFD method, a grouping condition is optimized by using a particle swarm algorithm, and the grouping condition is assigned to a corresponding controller. A BPPC algorithm and an MBIST test method are combined, and an optimal solution with the minimum number of controllers is sought under the limitation of the memory distance and the maximum power consumption. According to the method, a multi-stage optimization method and a multi-target method are adopted, so that the grouping result is more accurate.

The invention discloses a dynamic window-based graph optimization GNSS (Global Navigation Satellite System) and IMU (Inertial Measurement Unit) integrated navigation method, which improves the adaptability, calculation efficiency and positioning accuracy of a system in a dynamic environment by introducing a dynamic window strategy and a key frame mechanism. According to the method, the size of a sliding window is dynamically adjusted according to a real-time motion state, environment change and computing resources: when the environment is complex or high precision is needed, the window is enlarged to contain more historical information; when the environment is stable or computing resources are limited, windows are reduced to improve computing efficiency. Meanwhile, threshold values of position and posture changes are set, and only when the variable quantity of the current state and the latest key frame state exceeds the threshold values, new key frame nodes are added, key motion information is captured, and redundant calculation is avoided; according to the method, the optimal balance between the positioning precision and the calculation efficiency in a complex dynamic environment is realized, the method is suitable for an embedded platform with limited resources, and the requirement of high-precision real-time navigation is met.

The invention provides a heat sink preparation optimization method and device and a readable storage medium, and the method enables the process parameters of process control models corresponding to at least two adjacent deposition layers in the same heat sink to be different. The process parameters of the process control models corresponding to the deposition layers formed in the heat sink generation process and the performance parameters of the heat sink are collected to generate multiple sets of samples, and the models corresponding to the different deposition layers are trained through the samples and qualification judgment of the samples, so that the process control models are improved to optimize the performance of the deposition layers. And finally, the purpose of optimizing the comprehensive performance of the heat sink is achieved. The scheme provided by the invention is applicable to preparation of heat sinks with various thicknesses, the problems of defects and non-uniform performance in the production of the heat sinks can be effectively avoided, diversified and differentiated effects can be embodied on each deposition layer of the heat sinks, the different deposition layers can have the advantage of different directionality, and the production efficiency of the heat sinks is improved. And thus, the finally generated heat sink is ensured to have more excellent comprehensive performance.

The invention relates to an event-driven tactile sensing method and system based on a pulse graph neural network, and belongs to the technical field of tactile sensing and robot control. The method comprises the following steps: S1, extracting main features from data collected by an event-driven touch sensor; s2, encoding the main features extracted from the tactile sensor data; s3, using a spiking neural network to further learn feature embedding; s4, the encoded features are processed through a graph neural network; and S5, identifying the category or state of the target object by adopting a voting mechanism. According to the scheme, through the graph neural network, the graph based on the space structure of the tactile sensing unit can be automatically constructed, and local and global features are efficiently extracted; the pulse neural network is used for processing event-driven data, so that the energy consumption is remarkably reduced and the calculation efficiency is improved; the method has strong generalization ability, can adapt to different tactile sensor configurations and application scenes, and has excellent performance when processing real-time tactile perception tasks.

The invention provides a segmented rendering method based on three-dimensional Gaussian sputtering, and relates to the technical field of scene rendering, and the method comprises the steps: carrying out the partitioning processing of the point cloud data of a scene through employing an axis alignment bounding box, and obtaining a plurality of non-overlapped partitioned scenes; detecting intersection points of the light and the boundary of the axis alignment bounding box, and segmenting the light based on the intersection points to obtain a plurality of light segments; distributing a part of scenes through which the segmented light segments pass to corresponding 3DGS modules for rendering to obtain a block scene rendering result; combining the scene rendering results of the blocks to obtain an overall scene rendering result; the scene is divided into a plurality of non-overlapped block scenes, so that redundant calculation is avoided, and the calculation efficiency is improved; light rays are segmented and distributed to corresponding 3DGS modules, so that efficient management of the light rays among different block scenes is realized; and the block scene rendering results of the block scenes are merged through the segmentation rendering equation, so that the overall visual effect of the scene is ensured.

The invention discloses a keyword clustering extraction method based on segmented recursion and a multi-query attention mechanism, solves the problems of long text input limitation and context semantic disorder caused by factors such as context, cultural differences and language structures, and introduces two important mechanisms of segmented recursion and relative position coding. The method aims to construct the relation between the fragments, and the latter enables the position information of the vocabularies to be effectively integrated during segmentation processing, and the layer output of the previous fragment is cached and is reused in the processing of the next fragment, so that a large amount of redundant calculation is avoided, and the input limitation of a long text is solved. Relative position coding can play a better role in processing a long-distance dependency relationship and generalizing to sequences with different lengths. According to the multi-query cross attention mechanism, by sharing Key and Value weights, parameter operation occupation is reduced, and the reasoning speed of the model is increased.

The invention relates to the technical field of three-dimensional data processing, and discloses a three-dimensional grid data processing and multiplexing method and system, and the method comprises the steps: S1, obtaining and analyzing grid data, obtaining three-dimensional grid original data, and carrying out the format recognition, geometric analysis and topology analysis of the three-dimensional grid original data; s2, carrying out reuse-oriented standardization processing; s3, constructing multiplexing description information; s4, grid resource storage and index management; s5, determining a subset based on the request; s6, on-demand conversion and result generation; and S7, unified scheduling control. By constructing multiplexing description information containing a unique identifier, a hierarchy mapping relationship, a block mapping relationship, version information and a service adaptation mark, unified management and efficient multiplexing of grid data under different hierarchy and service scenes are realized, and unified data semantic support is provided for hierarchical retrieval, on-demand extraction and multi-service collaboration; therefore, the reusability of the three-dimensional grid data and the system cooperation efficiency are remarkably improved.

The invention provides a directional ship detection method and system based on joint attention. The method comprises the following steps: acquiring image acquisition data; performing feature extraction by using a backbone network to obtain a multi-scale feature map, performing feature re-calibration on the multi-scale feature map, and outputting an enhanced target feature map; carrying out convolution operation on the enhanced target feature map by adopting self-adaptive geometric convolution to obtain ship direction characterization information; generating a candidate frame based on the ship direction representation information, calculating intersection-parallel-ratio statistical characteristics in combination with a ship real frame, and performing training sample adaptive distribution according to the intersection-parallel-ratio statistical characteristics; based on an Anchor-free detection framework, carrying out ship orientation detection by utilizing the training sample subjected to self-adaptive distribution to obtain a ship orientation detection result; through multi-scale feature re-calibration, adaptive geometric convolution and dynamic sample distribution, the ship direction and features in a maritime monitoring scene can be captured, and the detection precision and real-time performance are improved.

The invention discloses a building vector graph multi-scene intelligent simplification method and system, and belongs to the technical field of geographic information systems. Comprising the following steps: establishing a four-level data system comprising an original data layer, an engineering cache layer, a display cache layer and a terminal view layer; monitoring data change and user interaction events through an event bus; the event is responded, the current application scene is intelligently recognized, and the corresponding simplified parameter configuration is dynamically loaded; based on the configuration, executing simplification logic on the target building vector graph; querying a topological relation to carry out cooperative processing on the contours of the adjacent buildings; and synchronously updating a processing result to the four-level data system. Through four-stage data caching, dual-drive triggering and scene self-adaptive judgment, efficient, high-quality and self-adaptive processing of building simplification in multiple scenes such as cartographic integration, real-time display and spatial analysis is realized, and the system flexibility, the resource utilization rate and the user experience are improved.

The application relates to the technical field of underwater robot vision processing, and discloses a GAN-based underwater robot image real-time repairing and target identification method. The method acquires an original image sequence obtained by an optical sensor of an underwater robot, extracts degradation features of each frame of image, including scattering noise distribution, color distortion parameters and motion blur intensity. The original image sequence is input into a network, and based on the degradation features, a repaired image is generated frame by frame, and local structure consistency features and global semantic coherence features of the repaired image are extracted. A cascaded classifier is used for multi-scale target detection of the repaired image, local structure features and global semantic features are combined, and a target boundary box and a category label are output. According to the spatial distribution density of the target boundary box, the priority of the repaired area is dynamically adjusted, and the synchronous output of the repaired result and the target identification result is realized. The method can effectively improve underwater image quality and target identification precision.

This application provides a speech synthesis method, a vocoder, and its training and optimization method. The vocoder uses a ConvNext neural network as its backbone network. ConvNext replaces transposed convolution with channel upsampling, avoiding the periodic noise of transposed convolution. It employs depthwise convolution and separable convolution, which not only captures the time-frequency information of the vocoder input but also significantly reduces the computational load of the backbone network. Using frequency band fusion technology, the vocoder can simultaneously output audio from different frequency components of the synthesized audio, thereby reducing the output sampling rate and improving the vocoder's operating efficiency. Specifically, the vocoder uses multi-channel output, with each channel corresponding to an audio signal within a specific frequency band. The frequency ranges of the signals from each channel are orthogonal to each other, and the sampling rate is 1 / M of the original (M is the number of frequency bands). Finally, the audio signals from each frequency band are anti-mirror filtered and superimposed to obtain the audio at the original sampling rate.

Embodiments of the invention disclose an information retrieval method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining a to-be-processed character string; if the length of the to-be-processed character string exceeds a set length value, according to the set length value, the to-be-processed character string is split in a non-repeated mode, a plurality of splitting results are obtained, the length of the to-be-processed character string is the number of characters contained in the to-be-processed character string, and the set length value is a positive integer larger than 1; retrieval is carried out on the basis of the multiple splitting results, multiple preliminary retrieval results are obtained, and each preliminary retrieval result comprises the multiple splitting results; and for each preliminary retrieval result, determining a screening result of the preliminary retrieval result based on the distance between two adjacent splitting results in the plurality of splitting results in the preliminary retrieval result. In the embodiment of the invention, the to-be-processed character string is split in a non-repeated manner, so that redundant calculation of repeated characters can be avoided, and the information retrieval efficiency is improved.

This invention proposes a method and system for diagnosing open-circuit faults in three-phase inverter IGBTs, relating to the technical field of power electronic device fault diagnosis. The method involves lightweight processing of acquired historical data to obtain feature vectors; training a pre-defined three-phase inverter IGBT open-circuit fault diagnosis model based on these feature vectors; real-time acquisition of three-phase current data to be processed, followed by bounded normalization to obtain normalized current amplitudes; and generating IGBT open-circuit fault judgment results based on these normalized current amplitudes. Real-time data related to the three-phase inverter IGBT open-circuit fault is collected based on the IGBT open-circuit fault judgment results, and this real-time data is input into the trained three-phase inverter IGBT open-circuit fault diagnosis model to output the three-phase inverter IGBT open-circuit fault diagnosis results. This invention effectively achieves lightweight, efficient, and accurate diagnosis of three-phase inverter IGBT open-circuit faults.

The invention discloses a deep learning-based intelligent recognition method for a motion mode of an unmanned surface vehicle. The method comprises the following steps: acquiring original sensing data in real time through a multi-source sensor system mounted on the unmanned surface vehicle, wherein the multi-source sensor system at least comprises an inertial measurement unit (IMU), a global navigation satellite system (GNSS) receiver, a laser radar and a visible light camera; performing time synchronization and coordinate system alignment processing on the original sensing data to generate a fused sensing data stream with a unified timestamp and a spatial reference system; dividing the fused sensing data stream into time window segments with fixed lengths, wherein each time window segment corresponds to one to-be-identified motion state sample; the IMU, the GNSS, the laser radar and the visible light / infrared camera are synchronously integrated, quadruple sensing dimensions are formed, the IMU captures instantaneous acceleration and angular velocity changes and reflects control instruction execution, the reliability of single-point observation is improved, and the system can still maintain the basic recognition capability when part of sensors fail.

This invention provides a rapid landslide area segmentation method based on high-resolution satellite remote sensing data and deep learning algorithms, belonging to the field of ground scene technology. The invention downsamples the original satellite remote sensing image to obtain a first image, extracts reflectance data from the red and near-infrared channels, calculates the reflectance difference and sum of reflectance values, and normalizes them to obtain a vegetation index to construct a vegetation layer. The first image and the vegetation layer are superimposed and stitched together, and feature enhancement is performed to obtain a vegetation feature map. This map is input into a convolutional neural network to output a landslide probability map. A two-dimensional coordinate set is constructed and cropped to obtain high-resolution test patches. The spatial gradient magnitude of the local vegetation index is calculated and weighted by a preset enhancement coefficient to generate edge-enhanced patches. The edge-enhanced patches are input into a semantic segmentation network to output a local landslide map, which is then backfilled into the initial image to obtain a landslide area segmentation map. This invention constructs a cascaded segmentation system to achieve efficient and accurate segmentation of landslide areas.

The invention discloses an annual hydropower and electric quantity distribution method and system considering monthly coordination, and relates to the field of medium and long term power and electric quantity balance analysis, and the method comprises the steps: obtaining wind power historical output curve information, considering the probability distribution characteristics of wind power output, solving upper and lower limit output values in a confidence interval, establishing an annual hydropower and electric quantity distribution model considering monthly coordination, and outputting a monthly decomposition result of hydropower and electric quantity; and carrying out statistics on the output condition at each moment, and calculating the renewable energy consumption condition. According to the method, the monthly coordination constraint of the reservoir is considered, and the monthly coordination effect of water resources all the year round is achieved; the mode that monthly loads in an existing annual electric power and electric quantity balance model are represented by single typical daily loads is adopted, and large-scale redundancy calculation in the whole time period is avoided while it is guaranteed that the result has a good application effect. And the result of the wind power output probabilistic model is fused into the hydroelectric power decomposition model, so that the adaptability of the hydroelectric power decomposition scheme to the new energy power system is improved.

This application provides an intelligent monitoring method and system for engineering construction, relating to the field of engineering construction. The method includes: collecting multimodal raw data streams; converting the raw data streams into pulse events, each pulse event containing event type, timestamp, spatial coordinates, and semantic tags; binding pulse events of different modalities into spatially correlated pulse event clusters, and analyzing the temporal characteristics of these pulse event clusters in real time using a spiking neural network, triggering data processing tasks only when abnormal patterns or critical events are detected; dynamically adjusting sensor thresholds and event binding rules using the temporal dependence plasticity of pulses; binding the bound pulse event clusters to BIM model components to generate digital twins with spatiotemporal tags, and generating control commands through the digital twins. This solves the technical problems of redundant consumption caused by indiscriminate data processing and the formation of data silos by independent modules.

The invention discloses an intelligent blank label image recognition and composite coding system, and relates to the technical field of image processing. The system comprises a label image feature processing part, an embedded code part and a composite code generation part, the label image feature processing part is used for obtaining multi-dimensional features of a target label image; curvature flow feature analysis is carried out, and local curvature distribution is calculated; calculating a geometric invariant moment of the target label image based on the local curvature distribution; the embedded coding part is used for extracting continuous homology-based topological characteristics of a target label image; according to the topological features, feature popular codes are constructed by using the Gaussian-Blog theorem; and the composite code generation part is used for carrying out unique coding based on differential topology by adopting a Hessian matrix according to a feature popular code and a fusion result. According to the method, the geometric and topological characteristics of the label image can be accurately identified, and a code with uniqueness and robustness is generated.

The invention relates to an intelligent registration and fusion method of power line patrol close-range non-coaxial spectral images, which finally realizes high-precision fusion of spectral images through region-of-interest identification, gridding feature point detection, nearest neighbor matching, random consistency sampling, image transformation and batch processing. According to the method, high-precision image registration of non-coaxial close-range images can be effectively achieved, the average comprehensive mutual information value exceeds 0.8 on average, pixel-level spectral image information fusion can be achieved, and high-quality registration and information fusion are basic prerequisites for insulator surface state enhanced imaging and spectral reconstruction imaging. Meanwhile, a large amount of redundant calculation and registration errors caused by complex background image feature point matching can be avoided, the calculation efficiency is greatly improved, and in-situ analysis and real-time imaging under an unmanned aerial vehicle airborne camera are facilitated. The method is high in automation degree and high in adaptability, and the efficiency and accuracy of power line patrol image processing are remarkably improved.

The invention discloses a severe environment unmanned aerial vehicle aerial photography target detection method based on multispectral iteration enhancement, and belongs to the technical field of computer vision. The method comprises the following steps: firstly, acquiring RGB pictures and infrared pictures in severe environments such as rain and fog, night and the like, and constructing a data set; then, an unmanned aerial vehicle aerial photography target detection model is constructed, and efficient deep fusion of visible light and infrared multispectral features is realized by introducing a novel iterative hierarchical attention and differential enhancement fusion framework; meanwhile, a tail enhancement unit of an HGBlock module in the backbone network is redesigned; then training an unmanned aerial vehicle aerial photography target detection model by using the data set; and finally, using the trained detection model to detect an aerial target of the unmanned aerial vehicle and evaluate the performance of the model. Through a fusion mode of combining interactive alignment, differential enhancement and iterative feedback, the target detection performance and real-time performance of the unmanned aerial vehicle under complex weather and illumination conditions are improved.

Embodiments of the present application disclose a same-page detection method, device and equipment and a storage medium. The method comprises: performing target region detection on a currently photographed page image to obtain a target object region; extracting a local image region from the target object region, and determining local feature points corresponding to the local image region; calculating a matching success rate of the local feature points and image feature points of a stored historical page; and determining that the historical page and the page image are the same page when the matching success rate is greater than a preset success rate threshold. The present application scheme does not need to perform a complete correction process on the entire image, but only needs to extract a local image region and local feature points for matching, so that whether the currently photographed page and the historical page are the same page can be quickly and accurately determined, redundant calculation caused by repeated correction and information extraction is effectively avoided, user waiting time is reduced, and processing efficiency and user experience are improved.

The application provides a cockpit implementation method and system based on emotion enhancement and scenario service flow intelligent agent, and belongs to the technical field of automobile intelligent cockpits. The method comprises the following steps: acquiring multi-modal data of a target user in real time, fusing the multi-modal data to generate comprehensive user states, planning a service flow based on scenario semantics and the user states, and generating and outputting action execution instructions of internal and external resources according to the service flow. Through multi-modal deep fusion and scenario semantic understanding, the application can accurately capture user states and demands, provide the most suitable service at the most appropriate time, and change passive response into active care. The application sets a user memory vector library to continuously learn and adapt to user preferences, provides a truly 'thousand faces for a thousand people' service, and as the use time increases, the service accuracy and user satisfaction present a continuously rising curve, thereby solving the problem of static experience of traditional systems.

The invention relates to a complex meteorological condition-oriented building natural ventilation evolution iterative design method and device and electronic equipment, and the method comprises the steps: building a three-dimensional model of a corresponding building, and obtaining the indoor volume of a closed geometry in the three-dimensional model; the initial opening sash model and the building main body model are operated, and a middle opening sash setting rule meeting the ventilation requirement is obtained; establishing a simulation working condition according to the hourly meteorological parameters and carrying out hourly analog simulation to obtain the ventilation quantity of a preset analysis time period in the sub-region; according to the indoor volume of the corresponding sub-region, the number of ventilation times in the preset analysis time period and the average number of ventilation times of the building main body model are obtained; according to the method, an opening fan setting scheme with the highest ventilation frequency under a set condition is searched in combination with algorithm evolution iteration, more effective partition processing is carried out on a complex building space by applying a multi-region grid method, and through multi-working-condition analog simulation and weighted averaging of hourly meteorological data, the multi-working-condition meteorological data is obtained. And carrying out quantitative evaluation on the natural ventilation effect of the design building in a given analysis time period.

The application discloses a virtual anchor real-time driving system based on facial motion capture, aims to solve the problems of insufficient precision, lack of naturalness and weak scene adaptation of virtual anchor facial motion in the prior art, and through multi-thread parallel collection of audio and video streams, optimizes data quality through adaptive preprocessing; adopts multi-dimensional facial feature collaborative extraction, double-branch phoneme recognition, face function area semantic segmentation and confidence dynamic weight adjustment technology, realizes accurate representation and collaborative fusion of expression and lip feature; finally, through the lip-expression collaborative driving mechanism, the real-time driving characteristics of the adaptive virtual image are output; thereby effectively improving the precision and naturalness of virtual anchor facial motion, enhancing the complex scene adaptability, giving consideration to real-time response and low-cost deployment, and being applicable to virtual live broadcast, online education and other scenes, and having good application value.

The invention provides a microseism event identification method and system based on multi-dimensional feature fusion ensemble learning, and the method comprises the steps: collecting and processing microseism data, and constructing a multi-dimensional feature tensor; constructing a multi-dimensional feature fusion ensemble learning network model by using a feature fusion subnet and a target fusion subnet based on the multi-dimensional feature tensor; and based on the multi-dimensional feature fusion ensemble learning network model, identifying target micro-seismic data to obtain a micro-seismic event identification result. According to the method, efficient and accurate microseism event identification is realized through collaborative operation of data preprocessing and the secondary subnet, and the application value is remarkable.

This invention relates to the field of image data processing, specifically to a wavelet-enhanced adaptive segmentation method and system for deep-sea manganese nodule images. Addressing the problems of boundary loss, region adhesion, and difficulty in small target identification in complex underwater environments such as low illumination and strong scattering, existing technologies construct an adaptive segmentation model based on an encoder-decoder architecture. This model first performs hierarchical feature extraction through a feature extraction module; then, it uses a feature pyramid module to fuse deep semantics and shallow detail information from the hierarchical features; furthermore, it combines a wavelet enhancement module to perform frequency domain decomposition and adaptive resolution adjustment, and relies on a multi-scale adaptive representation module to achieve spatial and channel dual-branch weighted fusion, ultimately outputting a high-precision segmentation probability map. This invention effectively overcomes the performance bottleneck in complex deep-sea scenarios, significantly improving the accuracy, robustness, and engineering application potential of manganese nodule segmentation.

The application discloses a self-adaptive wordpiece table level distillation method for a large language model, comprising the following steps: obtaining wordpiece probability distribution of teacher-student models at each generation step; extracting the union of the first K wordpiece indexes with the highest probability of each step respectively, and constructing an aligned candidate wordpiece set; calculating the absolute value of the teacher-student probability difference as the learning difficulty score of each candidate wordpiece, and collecting and constructing a global difficulty matrix; selecting high difficulty wordpieces from the difficulty matrix according to the current focus ratio to form a target distillation wordpiece set, and re-normalizing the student model probability; only calculating the divergence loss of the target wordpiece set and updating the student model parameters; and dynamically adjusting the focus ratio according to the real-time loss of the student during the training process. The application concentrates computing resources on difficult and high information wordpieces through dynamic self-adaptive screening, reduces the computing power consumption, improves the knowledge transfer accuracy, and is compatible with various distillation targets, and is suitable for deployment of large language models on resource-limited devices.

The invention relates to the technical field of biological information processing, in particular to a bar code correction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a to-be-corrected target bar code; generating a mode character string at each base position included in the target bar code, and replacing the base at the current position with the mode character string to obtain a preset number of new target bar codes; matching the new target bar code with each mode candidate set constructed by the bar code white list, and after the target mode candidate set is matched, matching the new target bar code with each new reference bar code in the target mode candidate set to obtain a first matching result; and correcting the target bar code based on the first matching result, and determining an inclusion relation between the target bar code and the bar code white list. According to the embodiment of the invention, index mapping is realized through the position mode, redundant calculation for generating a certain number of mutants is avoided, the query complexity is reduced, and the query speed is increased.

The present invention discloses a real-time basketball goal detection and recognition method based on video input, and specifically relates to the technical field of basketball goal detection. A fixed frame length impact assessment model is constructed according to basketball flight trajectory information, player pitching behavior information and image processing delay information. The system can adjust the initial fixed frame length in real time to ensure that the key picture of the goal moment is captured, and will not cause missed judgment or misjudgment of the goal moment due to the inability of the fixed frame length to adapt to the different durations of fast shots or slow shots, avoid misjudgment caused by the inability of the fixed frame window to adjust according to the actual duration of the shooting action, and improve the detection accuracy of the basketball goal status; through adaptive regulation, the system can be dynamically adjusted according to the real-time video input, reduce unnecessary waste of computing resources, avoid redundant calculations, ensure that it can quickly respond and accurately identify the basketball goal status in different scenarios, and adapt to the diverse actions and complex backgrounds in basketball games.