20results about How to "Reduce operational overhead" patented technology

The invention belongs to the field of mass spectrum imaging, and particularly provides an image segmentation method for a mass spectrum imaging image of an ESI source, and the method comprises the steps: (S1) dividing the mass spectrum imaging image into a plurality of mass spectrum imaging channels according to a mass-to-charge ratio; (S2) selecting a mass spectrum imaging channel where target ions are located, and preprocessing each pixel; (S3) carrying out preliminary segmentation on the spectrogram data; (S4) performing interval division on the mass spectrum data of the channel where the target ions of each pixel are located, calculating the lower area of each interval, selecting the central mass-to-charge ratio of each interval as the abscissa of the intensity value of each interval, and setting the intensity value of the interval with the ratio of the central mass-to-charge ratio to the highest intensity value lower than a set value as 0; (S5) analyzing, training and optimizing the transformed mass spectrum data; and (S6) the optimal combination model obtained in the step (S5) is used for classification of other pixel points of the mass spectrum imaging image, and an image segmentation result is obtained. The method has the advantages of accurate segmentation and the like, and is applied to mass spectrum imaging of the ESI source.

The invention discloses an Internet of Things equipment position sensing and superposed pilot frequency transmission method, which relates to the technical field of Internet of Things, and comprises the following steps: firstly, constructing a wireless frame structure comprising two parts of position sensing and superposed pilot frequency transmission; secondly, based on a wireless frame structure, a user carries out position estimation according to a base station pilot signal reflected by a reconfigurable intelligent surface RIS; then, on the basis of a position estimation result, a closed expression of the uplink reachable rate of the user is established; then, according to the closed expression, constructing a weighting and rate maximization problem for optimizing the RIS phase, and solving by utilizing a genetic algorithm to obtain an optimized RIS phase; then, based on the optimized RIS phase, the user sends a pilot frequency and a data superposition signal, and the base station performs channel estimation after receiving the superposition signal; and based on the channel estimation result, the base station performs data detection by adopting a maximum ratio merging detector. According to the method, under the condition that spectrum resources and transmission power are limited, the UE position can be accurately obtained, and the system transmission rate is remarkably improved.

The invention belongs to the technical field of array signal processing, and relates to a polarization sensitive array radar adaptive snapshot number angle measurement method. The method specifically comprises the following steps of: setting an initial azimuth angle, a Cramer-Rao Bound (CRB) of a pitch angle and an initial snapshot number; a far-field narrow-band signal is set to be incident to a polarization sensitive array of any configuration, and an array receiving data covariance matrix is calculated; performing characteristic decomposition on the covariance matrix to obtain a noise subspace; estimating a target angle and a polarization parameter by using orthogonality of a noise subspace and a target steering vector; on the basis of Cramer-Rao bound derivation of the azimuth angle and the pitch angle of the polarization sensitive array, the relation between the azimuth angle CRB and the snapshot number and the relation between the pitch angle CRB and the snapshot number are obtained, and the minimum snapshot number meeting the CRB requirement is calculated through the estimated target angle, polarization parameters and the Cramer-Rao bound of the set initial azimuth angle and pitch angle; and selecting receiving data corresponding to the snapshots according to the calculated minimum number of snapshots, and carrying out angle and polarization parameter estimation.

The invention discloses an embedded firmware camera data modification method and device, a medium and equipment, and relates to the technical field of computer vision, and the method comprises the steps: collecting image data in real time, and carrying out the primary processing; a frame difference method and a background modeling algorithm are adopted to carry out region division and priority distribution on the image data after primary processing, and a region priority mapping table is generated; extracting feature points of a high-priority motion area in the optimized image data through an optical flow method, and calculating a motion vector between continuous frames; according to the calculated motion vector between the continuous frames, an event-driven frame processing mechanism is triggered, and the frame rate and the processing precision are automatically adjusted; through the motion vector extracted through the optical flow method, the motion speed of the object can be calculated in real time, the frame rate and the processing precision are dynamically adjusted according to the motion speed, it is ensured that enough details are captured in the rapid motion process, and processing resources are saved in the static process.

The invention discloses a fine-grained security access control method and system, computer equipment and a storage medium, and the method comprises the steps: obtaining a public parameter set through initializing global parameters, initializing each attribute authorization mechanism, and generating an attribute private key and an attribute public key corresponding to attributes administered by the attribute authorization mechanism; encrypting a plaintext, and encrypting a key of an encryption algorithm according to an access control policy defined by a data owner; acquiring a private key which is generated by an attribute authorization mechanism for each user and is included in an attribute administered by the user, and sending the private key to the user in a synthesized private key form through a key exchange protocol; decrypting the ciphertext according to the private key of the corresponding attribute set; or generating an outsourcing decryption key, and decrypting the ciphertext according to the outsourcing decryption key. The method is suitable for fine-grained security access control supporting attribute value comparison of cloud data sharing, and can provide a secure and low-sales outsourcing decryption scheme on the basis of supporting a complex access control strategy.

The application discloses a federated learning method and system based on modulus component homomorphic encryption and response verification, relates to the technical field of information security, and comprises the following steps: obtaining initial global model parameters and generating a pre-shared key; a client generates a participation key and a calculation key; a plurality of clients are randomly selected as participation clients and a challenge pair is sent; a participation client generates a challenge response value based on the pre-shared key and the challenge pair, performs local training based on the initial global model parameters, encrypts local model parameters based on the participation key and a modulus component homomorphic encryption algorithm, and obtains model parameter ciphertext; trusted verification is performed based on the challenge response value, and encrypted global model parameters are obtained by aggregating trusted model parameter ciphertext based on the calculation key; the participation client updates the local model based on the encrypted global model parameters, the above process is repeated until the model converges, and a trained global model is obtained. The encryption calculation efficiency, response speed and security robustness of the federated learning are improved.

This invention discloses a lidar depth completion method based on hierarchical minimum surface reconstruction, relating to the fields of computer vision and image processing technology. The method includes: performing depth value inversion and morphological dilation on a sparse depth map to obtain a scene dilated depth map and a full-resolution effective mask; then downsampling to obtain a coarse-scale scene dilated map and a coarse-scale mask; using an iterative convolution kernel of the Laplacian operator to obtain a coarse-scale filled map; upsampling the coarse-scale filled map to obtain an upsampled depth map, and then fusing it with the scene dilated depth map to obtain a fused depth map; locating all remaining holes in the fused depth map using connected component analysis, and calculating the mean depth of the effective pixels within the annular pixel band around each remaining hole; using this mean depth to fill the corresponding remaining holes to obtain a hole-free depth map; and after global Gaussian blurring, performing a depth value inversion operation to obtain a scene dense depth map, thereby achieving high-precision 3D reconstruction.

The present invention discloses a non-contact vital sign detection system and method based on video images, belonging to the field of image processing technology. The system of the present invention includes a multi-core heterogeneous microprocessor, and the multi-core heterogeneous microprocessor includes a programmable control system and a programmable logic system; the programmable control system is composed of a microprocessor, and the programmable logic system is composed of a programmable logic circuit; the method of the present invention adopts a multi-core microprocessor to simplify the image and signal processing algorithms and processes involved in non-contact vital sign detection, and parallelizes and accelerates key algorithms with high computational complexity such as face detection required for non-contact vital sign detection, and reduces the algorithm's computing power requirements and resource overhead while maintaining the detection effect, so that it can run quickly and stably in a low-cost embedded platform, reducing cost, volume and energy consumption, and is highly portable, convenient for users to operate and use, and broadens the application scenarios of non-contact vital sign detection products.

The application provides a list component state synchronization method, system, medium, equipment and program product, relates to the terminal application technical field, and includes the following steps: when a scrolling operation is listened to, an offset is obtained from a global state manager before a sub-item mark is visible and is added to a dynamic linkage pool; a component controller is registered to join the linkage pool to receive a broadcast; when the broadcast is received, a current driver pointer is added to the sub-item through the global state manager; and the scrolling is performed in combination with the offset and the driver pointer. The application can avoid the inconsistency problem of rendering and data binding timing when a virtual list is high-speed scrolled, accurately mark an event source through a driver pointer, block an event loop of multi-way linkage without a state lock, simplify state management and control, avoid deadlock, and comprehensively improve the use experience of the virtual list in a complex scene.

This invention belongs to the field of lithium-ion battery management and provides a lithium-ion battery management method and system based on a visual large language model. The method involves acquiring battery operation sampling data and running a pre-built battery physical model. Based on the battery operation sampling data and the battery physical model, state estimation is performed. Based on residual window statistics, instantaneous residual extreme values, and operating condition anchor points, it is determined whether a battery management event has been triggered. When an event is triggered, a multi-trajectory residual panel plot is generated, forming a structured numerical summary. A query vector is constructed based on the current event, and similar historical cases are recalled from a pre-built task-partitioned enhanced knowledge base. The residual panel plot, structured numerical summary, operating condition anchor points, and historical cases are used as multimodal inputs, and the visual large language model is used to determine the task route. The corresponding execution branch is then entered based on the task route. This invention ensures real-time state estimation and avoids large-model inference on every frame of high-frequency data.

The invention discloses a multi-spectrogram lightweight acoustic scene classification method and device for auditory auxiliary equipment, equipment and a storage medium. The method comprises the following steps: firstly, acquiring a hearing-aid front-end environment audio signal to be processed, converting the hearing-aid front-end environment audio signal into a Mel spectrum feature map, a constant Q transformation spectrum feature map and a first-order difference spectrum feature map, and then stacking the Mel spectrum feature map, the constant Q transformation spectrum feature map and the first-order difference spectrum feature map to form a multi-channel spectrum feature map containing complementary time-frequency information as model input; then differential shallow layer feature extraction is carried out through an asymmetric multi-stream module, and depth features are extracted on a low-power-consumption computing platform by using a lightweight backbone network with a ghost bottleneck module and coordinate attention as cores; a spectral morphology module, a frequency band attention module and a channel weight balance fusion module are introduced, and multi-dimensional enhancement and dynamic weighting are carried out on the depth features; and finally, outputting an audio scene classification result by the classifier. According to the method, the classification performance of complex acoustic scenes can be improved while the model parameter quantity and the operation power consumption can be remarkably reduced.

The application discloses a mouth shape retrieval model optimization method, system and device based on time sequence context modeling, which is applied to the technical field of data processing. Multi-frame audio feature coding is completed through a time sequence window, one-dimensional convolution and weighted fusion, so that audio representation with time sequence consistency is generated. A retrieval preference model is constructed in combination with historical retrieval data, time sequence smoothing constraints and state transition rules are formed. A retrieval sorting model is built relying on a multi-objective optimization function, candidate screening is completed by comprehensively matching multi-dimensional matching items and superimposing various types of penalty constraints. Meanwhile, boundary adaptive weight regulation and time sequence stable reasoning strategies are added, and dynamic optimization is performed for pronunciation boundary, high-risk and low-confidence scenarios. The whole scheme runs in a frame-by-frame streaming mode, upgrades traditional single-frame optimal retrieval to time sequence context constraint optimal retrieval, effectively solves the mouth shape jitter problem, and improves the overall time sequence stability and output naturalness.

The present disclosure provides a blockchain-based Web3.0 access authentication method and a spatial information network system. A first satellite access node determines an access time interval. When the access time interval and the credit level of a mobile user both satisfy a preset fast access condition, the mobile user and the spatial information network system interact based on a historical session key. When the access time interval and / or the credit level of the mobile user does not satisfy the preset fast access condition, the first satellite access node and the mobile user perform two-way trust verification. When the mobile user and the first satellite access node both pass the trust verification, the client and the first consensus ground node obtain a session key. Thus, the access convenience of high-frequency compliant users is considered, and the invalid occupation of network computing power is reduced through differentiated verification strategies, achieving a balance between spatial information network security protection and access efficiency, and computing power optimization.

The application discloses a complex layout document image tamper detection method and system, and relates to the technical field of image processing and computer vision. Aiming at the multi-scale tamper detection problem of complex layout personnel file images, a credible layout analysis network is proposed. The network first extracts pixel gradient direction distribution features as multi-view inputs by using a differential operator through an image detail feature enhancement module; then, with the help of a twin encoder, multi-level features of visual layout and gradient distribution are extracted through a multi-scale attention mechanism; then, after passing through a multi-level dual-flow fusion module, heterogeneous modal features are deeply fused, and global context modeling decoding is completed in combination with a matrix decomposition mechanism; finally, a tamper detection head outputs a fine-grained multi-class mask, realizes joint optimization of layout element recognition and tamper state discrimination, and improves the accuracy and robustness of detection with the mutual constraint of the two. The application significantly improves the multi-scale tamper detection precision, and simultaneously has excellent layout analysis performance and running efficiency.

This invention discloses an indicator attribution analysis method based on expression tree decomposition and adaptive drill-down. The method includes: parsing the calculation expression of the target indicator to generate an operator tree; obtaining base period and current period dimension-grouped indicator data, and calculating the total change of the target indicator at the root node of the operator tree; calling the corresponding decomposition rules according to the operator type of each internal node, completing the operator decomposition layer by layer from the root node, satisfying the change conservation constraint throughout the process, and obtaining the attribution contribution of each dimension value in the current layer; calculating the attribution gain of a single dimension path, and generating drill-down or stop decisions for each path after independently comparing it with a preset threshold; continuing to drill down and repeating the calculation process for paths that meet the gain threshold, and stopping drill-down for paths that do not meet the threshold, finally generating an asymmetric attribution tree with differentiated branch depths, and outputting a structured attribution report. This invention can achieve accurate and verifiable indicator attribution, and balances analytical accuracy and computational efficiency through an adaptive drill-down mechanism.

The application discloses a plunger pump wear fault detection method based on pressure-temperature layered triggering and physical prior mask self-attention, and belongs to the technical field of hydraulic system state monitoring and fault diagnosis. The method comprises the following steps: collecting vibration, pressure and temperature multi-source signals, and setting a default low power consumption of a main controller; realizing layered early warning and deep diagnosis awakening based on four dynamic characteristics of pressure and temperature and an abnormal proportion of a sliding window; performing synchronous compression wavelet transform on the vibration signal and performing temperature-pressure analytical linear real-time correction, block encoding and vibration Token sequence generation; generating an additive physical mask guided self-attention calculation based on a fault frequency band prior, and extracting wear sensitive features; comparing with a working condition grouping fault feature library to determine the fault type and grade. The application runs through the whole chain of signal correction, feature fusion and diagnosis decision with physical knowledge, and under the lightweight design of a model parameter quantity less than 500k, realizes a variable working condition diagnosis accuracy of 94.2% and an early wear false negative rate of 3.5%.

A method for identifying and structuring terminal block wiring, collecting a substation terminal block content image, using OCR technology to perform text recognition, then performing number type recognition on the OCR-recognized text according to the position coordinates of the recognized text box and image features, and finally identifying the correspondence between the terminal group, terminal port and wiring according to the position of the text box, and deriving a structured result. The present invention solves the problem that the identification of substation terminal block wiring currently mainly relies on manual identification with low efficiency, and realizes rapid and accurate identification of substation terminal block wiring.

The present invention relates to an explicit guidance method for anti-interference fixed-point landing of a spacecraft, comprising: establishing a dynamic model in a LVLH coordinate system; establishing an interference observer, estimating the disturbance online to obtain an anti-interference term, and introducing it into a guidance instruction for compensation to obtain a disturbance-compensated model; constructing a guidance law for the disturbance-compensated model using an optimal principle, and solving the guidance instruction; solving to obtain an analytical range expression, and calculating a thrust correction value to achieve control of the anti-interference fixed-point landing of the spacecraft. The method of the present invention can ensure the guidance accuracy in the presence of uncertain interference, and has the advantages of small calculation amount and being able to achieve fixed-point landing.

A document processing method and device, storage medium, terminal, and computer program product, the method comprising: determining a document to be processed; parsing each page of the document to be processed to determine a content list and a directory list, wherein the content list contains multiple paragraph items, each paragraph item contains a single paragraph, and the directory list contains multiple title items, each title item contains a single title; constructing a directory tree based on the directory list; traversing the directory tree layer by layer, and performing text matching on the title indicated by the traversed current node and the title indicated by the adjacent node at the same level, respectively, with at least a part of the paragraphs in the content list to determine two matching paragraphs, and then splicing all the paragraphs between the two matching paragraphs in the content list, and using the splicing result as the associated text block of the current node. By adopting the above scheme, the document can be accurately divided into text blocks and an association relationship can be established between each title to obtain a tree-structured document.

This invention discloses a noise-resistant biomedical signal processing method, system, storage medium, and program product based on latent topological evolution. The invention acquires one-dimensional time-series biomedical signals and static auxiliary features, which are then fused and dimensionality-reduced using a dual-stream encoder to generate a global latent feature vector located in the Lie algebra tangent space. Geometric random perturbations are injected into the tangent space through an evolutionary dynamics system to generate an effective evolutionary velocity vector. A three-dimensional topological state orthogonal matrix is ​​generated using a single-step deterministic analytical mapping that avoids numerical orthogonalization iteration. The physiological signal state prediction results are output after classification and decoding, and control signals are generated. This invention avoids numerical iterative calculations through analytical mapping, significantly reducing computational load while maintaining accuracy. It is suitable for real-time edge monitoring and high-concurrency cloud processing scenarios, and can effectively eliminate sensor displacement artifact interference.