11results about How to "Reduce space complexity" patented technology

The invention relates to the technical field of wiring, and discloses a wiring method, electronic equipment and a computer readable storage medium, and the method comprises the steps: converting an initial layout layout of a circuit board into a grid chart; according to the grid chart, a coarse wiring result of the initial layout layout is determined, and the coarse wiring result is an initial wiring path meeting a wiring task and achieving a wiring cross minimization target; generating a wiring geometric figure according to the safety spacing and the initial wiring path, and generating a device geometric figure according to the safety spacing and component information in the initial layout layout; and under the limitation of the geometric figure of the device, optimizing the position of the geometric figure of the wiring by taking the wiring constraint as a target to obtain a fine wiring result. According to the method, the wiring process is divided into a coarse wiring stage meeting the goals of connectivity and minimum wiring intersection number and a fine wiring stage meeting the wiring constraint, so that the solving complexity can be reduced, and a wiring scheme with the minimum wiring intersection number can be obtained in a relatively short time.

This application provides a method, device, and storage medium for obtaining multi-party aggregated credit enhancement, relating to the field of data security, and is used by the i-th participating party; including: obtaining the credit enhancement query request and shared base sent by the querying party; sending the encryption index r to the querying party. i The enhanced credit query request is used to request enhanced credit information from the querying user; the encryption index of other participants sent by the querying party is obtained, and the encryption noise R of the i-th participant is obtained based on the common base and the encryption index. i , where; according to the encryption noise R i To obtain the ciphertext E of the user's plaintext security information from the i-th participant, encrypt the security information in plaintext. i The encrypted information E will be used to enhance the security information. i Send to the querying party; wherein, the encrypted information E is the enhanced information. i This invention is used by the querying party to obtain aggregated credit enhancement information, which is the sum of the plaintext credit enhancement information of the user from N participating parties. This invention enables the rapid acquisition of aggregated credit enhancement information while ensuring data security.

This invention provides a multi-channel continuous fast packet reception, sorting, and lock-free asynchronous read / write method, relating to the fields of computer communication and storage technology. The method includes: employing a dynamic expansion mechanism to continuously receive and store multi-channel data packets; classifying and sorting the stored data packets; and asynchronously reading and writing the parsed data packets in a lock-free manner. This invention generates an independent message stack for each channel, enabling continuous reception and storage of messages from multiple channels without pre-allocating excessive memory space. The speed of receiving buffer expansion is dynamically adjusted based on data flow rate. Fast classification and sorting of data packets is achieved using a hash table or sorted binary tree. A circular stack is created to sequentially store the parsed data content of each packet, updating the stack top position after data content is stored. When reading data, the starting position for reading is found using the stack top position and the number of packets read, and then the data is copied, thereby achieving lock-free asynchronous parallel data read / write.

The application discloses an ICS intrusion detection system and method fusing reinforcement learning and feature selection optimization, carries out binary coding and population initialization on data feature selection of an industrial control system (ICS) historical data set, carries out offline training through SVM-reinforcement learning, takes the accuracy obtained on a verification set as a fitness function, designs cross operation and mutation operation based on cumulative probability to update the population, and obtains an optimal feature set after iterative optimization; feature selection is carried out on an ICS real-time data set based on the optimal feature set, online intrusion detection testing is carried out on the real-time data set through support vector machine (SVM)-reinforcement learning, and thus an intrusion detection performance index is obtained. The application adopts a new mode based on SVM-reinforcement learning, and fuses intelligent optimization of optimal feature selection on this basis, and improves the intelligent design level and the precision of intrusion detection of the ICS intrusion detection system.

This invention discloses a signal detection method for an OTFS system based on an adaptive wavelet scattering convolutional neural network, comprising: converting the data symbols to be transmitted to the DD domain, and then obtaining an OTFS signal through an OTFS modulator; the OTFS signal reaching the receiver through a wireless channel; at the receiver, transforming the received signal back to the DD domain using an OTFS demodulator; extracting the real and imaginary signal matrices from the received OTFS signal, performing a three-layer wavelet scattering network transformation on these two matrices to obtain scattering feature coefficients for each layer, concatenating the scattering feature coefficients along the feature channel direction to reconstruct a high-dimensional feature matrix of the signal, and using it as the input to an adaptive wavelet convolutional neural network 2D-AWCNN to obtain the possible values ​​corresponding to each QAM symbol; and performing 16-QAM demodulation on the output of the adaptive wavelet convolutional neural network 2D-AWCNN using a demodulator to obtain the output bits used for signal detection.

This invention discloses a tree-based encoding method and system, belonging to the field of data structure representation technology. The method defines the encoding structure using letters and numbers; it binds encoding relationships by inheriting the parent node's encoding as a prefix for the child node's encoding, and concatenates this prefix with the child node's unique node to form the complete child node encoding; when generating encodings for added nodes, according to the encoding generation logic, it first determines the parent node and then the sibling nodes, and generates encodings in the order of the largest sibling node by sorting the sibling nodes; based on reflection, it calls the encoding function, establishes an Entity base class with necessary attributes and a rule class with generics, and implements the Class class through reflection. <iservice>The initialization of the tree structure. The application reduces the algorithm complexity of the tree structure inquiring all child nodes, and does not need to modify other nodes when adding and deleting, and guarantees the persistence of the database.< / iservice>

This disclosure provides a data storage method and apparatus. The method includes: acquiring target data to be stored; reading a bitmap structure from a database based on a target identifier in the target data; and determining the mode of the bitmap structure. When the bitmap structure is determined to be in a variable-length mode, writing a target time from the target data into a newly added time unit in the data portion of the bitmap structure, wherein the data portion of the variable-length bitmap structure contains at least one time unit. When the bitmap structure is determined to be in a fixed-length mode, setting corresponding data bits in the data portion of the bitmap structure based on the target time in the target data, wherein the storage location of each data bit in the data portion of the fixed-length bitmap structure corresponds to a time point. This method can reduce the space occupied by data storage, thereby reducing storage costs, and improve the efficiency of data retrieval, thereby reducing query latency.

The invention provides an autonomous navigation laser radar-camera fast fusion target detection method and device and a storage medium, and belongs to the field of multi-modal target detection, and the method comprises the steps: obtaining a laser radar point cloud and a camera image; obtaining point cloud BEV features according to the laser radar point cloud; performing feature extraction on the camera image to obtain a first image feature, and converting the first image feature into an image BEV feature by using a dynamic view angle converter; fusing the point cloud BEV features and the image BEV features to obtain fused BEV features; and a final target detection result is obtained. According to the invention, the operand of BEVPooling is reduced through the dynamic view angle converter, and the time and space complexity of the network structure is reduced, so that the reasoning time of the laser radar-camera fusion perception neural network is reduced, and the real-time performance of target detection is improved.

The application discloses a REC signal detection method based on OTFS modulation and deep learning, and the implementation steps are as follows: OTFS demodulation is performed on a REC signal; a target state information in the REC signal is detected by using an approximate message passing algorithm of sparse Bayesian learning; a channel is sequentially subjected to equalization operation and preprocessing by using the state information; a conditional generative adversarial network constructed is trained by using the preprocessed received signal; and the preprocessed received signal is sent into the trained conditional generative adversarial network to perform communication decision, so that a recovered REC communication signal transmitted by a UAV label of our side is obtained. The approximate message passing algorithm of sparse Bayesian learning is used to reduce the time complexity and space complexity of an algorithm in a target detection process, and the special properties of a channel matrix are used to reduce the inverse operation in an equalization process, so that the calculation complexity is further reduced, and the system operation efficiency is improved. The deep learning model is introduced into the communication receiver design, and the communication reliability can still be maintained when a high-speed moving target is used.

This invention discloses a method, device, and medium for security assessment of number-theoretical transformation modules based on bypass analysis. The method includes: collecting first and second bypass curves generated by the target device during number-theoretical transformation and subsequent polynomial multiplication, and constructing a leakage template accordingly; constructing a number-theoretical transformation factor graph based on the inter-layer mapping relationship of butterfly operations from layer i to L of the number-theoretical transformation; obtaining first and second probability distributions through template matching using the first and second bypass curves respectively; merging the two probability distributions as prior conditions to update and iterate the number-theoretical transformation factor graph; constructing a lookup table for the layer i butterfly operation; reversing the variable node values ​​of the preceding butterfly operation based on the updated factor graph, and recursively backtracking to the first layer input to recover the key; and finally assessing the security of the number-theoretical transformation module based on the recovered key. This method combines number-theoretical transformation factor graphs, probability inference, and lookup table reverse reasoning, improving assessment efficiency and accuracy.

The application discloses a lithium ion battery state of charge estimation method based on an Informer, which comprises the following steps: collecting battery sample data, constructing an Informer network model, processing the battery sample data by using the Informer network model, and obtaining a state of charge estimation value of the lithium ion battery. The Informer network model used in the application reduces the point product calculation of the attention mechanism and improves the encoder-decoder architecture, thereby reducing the calculation complexity and spatial complexity, improving the model training and calculation speed, and to some extent, solving the cumulative error problem existing in the recurrent neural network, achieving higher SOC estimation accuracy, showing superiority in MAE and RMSE error indicators, and better solving the estimation difficulty under low temperature conditions and the problem of insufficient temperature expansion capability. The application is widely applied in the technical field of batteries.