72results about How to "Reduce space complexity" patented technology

The invention belongs to the technical field of power grid main equipment diagnosis, and provides a power grid main equipment fault diagnosis method and system based on voiceprint cloud edge collaboration, which adopts a new cloud edge collaboration method and divides the analysis of voiceprint monitoring data into two stages. According to the method, the exception detection model is deployed on the edge side, exception analysis of mass voiceprint data is achieved, concurrent requests of a large number of concurrent signals at the cloud side are avoided, and the timeliness of voiceprint monitoring and analysis is improved. In the cloud, a multi-modal defect identification method fusing voiceprint, visible light, infrared and other multi-source data is deployed, secondary classification of sound abnormal signals is realized, and the reliability of equipment operation, maintenance and management is improved.

The invention discloses a power prediction method considering photovoltaic power clustering, which comprises the following steps of: firstly, acquiring historical data of a photovoltaic power station, decomposing a photovoltaic power time sequence by using an STL time sequence decomposition method, performing DBSCAN clustering on the decomposed photovoltaic power data, identifying a low-power cluster and a high-power cluster, and predicting the power of the photovoltaic power station according to the low-power cluster and the high-power cluster; establishing a photovoltaic power prediction model based on a time convolutional network and Times Net for low power and high power respectively; secondly, a badger algorithm is improved, Chebyshev chaotic mapping is adopted to improve initialization of a badger population, and a Gaussian mutation strategy and a lens imaging reverse learning strategy are introduced to obtain MHBA; and optimizing hyper-parameters of the TCN photovoltaic low-power prediction model and the TimesNet photovoltaic high-power prediction model by using an improved badger algorithm, and predicting photovoltaic power by using the optimized models to obtain a final prediction value. Compared with the prior art, the precision and effectiveness of the photovoltaic power prediction model can be improved.

The invention discloses a three-dimensional color modeling method and system based on multi-sensor fusion, and the method comprises the steps: obtaining monocular vision pose information, an RGB image and radar three-dimensional point cloud information, enabling the monocular vision pose information to be used for correcting a radar pose, and generating corrected point cloud information; the corrected point cloud information is screened and then projected to a specific plane; proper feature points are extracted from the projection image and the RGB image, and according to the corresponding relation between the feature points of the projection image and the RGB image, the plane point cloud of the projection image and the pixel coordinate plane of the RGB image are registered; performing fusion modeling on the point cloud and the image data by using an MRF model; according to the method, the radar pose is corrected by using visual pose information, the accumulated error influence of a system is reduced, the corrected point cloud is projected to a plane, and feature points of a projection image and a color image are extracted to establish an MRF model for describing the relationship between a pixel plane and the corresponding point cloud, so that a point cloud image which is uniformly distributed in space and contains color information is obtained.

The invention discloses an optical fiber terahertz communication system optimization method based on artificial intelligence, and belongs to the technical field of communication. According to the method, four-dimensional constellation shaping is constructed by utilizing additional dimensions provided by two independent sidebands, and an end-to-end optimization framework is divided into a training stage and a deployment stage; in the training stage, firstly, an equalizer based on a pruning neural network and an equalized channel model based on a mixed density network are trained in sequence through receiving and transmitting of a random training sequence; then connecting a constellation shaper at a transmitting end with a demapper at a receiving end through an equalized channel model with fixed parameters, thereby considering full-link residual distortion and noise after equalization, and carrying out back propagation gradient to learn a globally optimal four-dimensional joint geometric probability constellation diagram to further approach the Shannon limit; and in the deployment stage, transmission of the bit stream is realized through coordination of the four-dimensional geometric shaping constellation table and the probability distribution obtained in the training stage and an equalizer at the receiving end and a demapper.

The invention discloses a vehicle heat management system and a vehicle. The vehicle heat management system comprises an engine cooling loop, a low-temperature loop, a refrigerant loop, a warm air loop and a multi-way valve. Wherein the low-temperature loop comprises a battery loop and a motor loop, the multi-way valve is connected with the engine cooling loop, the warm air loop, the battery loop, the motor loop and the refrigerant loop, and different valve ports of the multi-way valve are switched and communicated to communicate part or all of the engine cooling loop, the warm air loop, the battery loop, the motor loop and the refrigerant loop; a single multi-way valve is arranged to be connected with an engine cooling loop, a warm air loop, a battery loop, a motor loop and a refrigerant loop, different valve ports communicated with the multi-way valve are switched according to the heat dissipation or heating requirements of all parts and spaces of a vehicle, and multiple heat management modes are achieved. The energy of the whole vehicle is efficiently utilized on the premise that the thermal management requirements of all parts of the vehicle are met, the number of the valve bodies is reduced, and therefore the space requirement and complexity of the whole thermal management system are reduced.

The invention discloses a reliability parameter assisted low-complexity polarization code rate matching method, and belongs to the technical field of wireless communication. Aiming at the problem that a proper punching site is difficult to select during rate matching due to a recursive structure of a polar code, the invention provides a polar code punching site selection algorithm based on a reliability parameter. An information bit reliability parameter is defined to calculate the reliability of candidate punching bits, and the optimal punching position is selected by traversing and solving the minimum value of the maximum reliability parameter. In order to solve the problem of high complexity of the existing polar code rate matching method, an interleaving algorithm and a pruning serial offset list decoding algorithm based on rate matching parameter mapping are provided, secondary sorting calculation of an interleaver is avoided, and a calculation unit updating strategy of a decoder is simplified. The polarization code rate matching method provided by the invention not only can improve the rate matching coding gain, but also can effectively reduce the calculation complexity.

The invention provides a vehicle railway slab detection method based on a laser radar, and the method comprises the steps: obtaining two-dimensional point cloud data of a railway slab sensed by the laser radar, wherein the two-dimensional point cloud data comprises a plurality of pieces of two-dimensional point data; determining the two-dimensional point data of which the scanning angle is a preset angle as an orthographic projection point, and traversing the two-dimensional point cloud data from the orthographic projection point according to an angle difference value of the scanning angles between the adjacent two-dimensional point data, and if the difference value of the scanning distances between the adjacent two-dimensional point data is greater than a distance threshold value, traversing the two-dimensional point cloud data from the orthographic projection point; and determining the two-dimensional point data corresponding to the smaller scanning distance in the adjacent two-dimensional point data as a first edge point and a second edge point of the railway slab. And based on a trigonometric function, determining a first distance between the first edge point and the orthographic projection point and a second distance between the second edge point and the orthographic projection point according to the scanning distance and the scanning angle of the first edge point and the second edge point. And detecting whether the vehicle deviates from the center of the railway slab according to the first distance and the second distance.

The invention discloses an end-side cloud system-oriented multi-task joint scheduling method, which comprises the following steps of: firstly, constructing an end-side cloud system multi-task joint scheduling model on the basis of uniformly describing nodes and tasks of an end-side cloud system; secondly, by taking maximization of the expected benefit of the multi-task joint scheduling as an optimization target, establishing an optimization problem solving constraint, and realizing construction of the optimization problem of the multi-task joint scheduling of the end-side cloud system; and finally, a mode of combining a heuristic strategy and nonlinear optimization solution is adopted to realize step-by-step decision of task execution nodes and task allocation resource quantity, and final generation of a multi-task joint scheduling scheme of the end-side cloud system is completed. According to the method, concurrent computation-intensive task joint scheduling highly suitable for the end-side cloud system can be realized from three aspects of multi-task joint scheduling model construction, multi-task joint scheduling optimization problem establishment and multi-task joint scheduling scheme generation.

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.

The invention relates to a multi-sub-mirror structured short video batch mixing and cutting method with controllable repetition degree of materials, which comprises the following steps of: separately managing sub-mirrors and the materials, establishing a mapping relation between the materials and the videos, introducing a control value of an allowable maximum repetition sub-mirror number during calculation and combination, and performing gradient repetition degree operation from low to high. Through tracking and statistics of historical materials, a material minimum usage priority principle is applied, and video library repetition comparison is converted into material repetition comparison; the method has the advantages that the historical use records of the materials are persistent, and the use condition of each material can be tracked; the output video combinations are output according to the gradient of the repetition degree from low to high, the material repetition index of the material combination of each video is visualized, and the risk of preferential release is reduced to the minimum; the situation that all combinations are compared with all videos in a video library one by one is effectively avoided, and the time and space complexity of a combination calculation algorithm is reduced.

A plant community multi-objective network optimization method in an intelligent logistics edge device completes intelligent logistics network multi-objective optimization by simulating behaviors of sowing, growing, flowering and fruiting of a plant community on the intelligent logistics edge device, and comprises the following steps: step 1, solving intelligent logistics network planning optimization by the plant community; step 2, optimizing a plant community solution intelligent logistics plan; step 3, performing optimization on plant community solution intelligent logistics execution; and step 4, optimization of the intelligent logistics reverse network for solving the plant community. According to the method, multi-objective optimization of the intelligent logistics network can be efficiently completed on the edge device.

The invention discloses a hexahedral mesh generation method based on image feature point local encryption, and the method comprises the steps: S1) obtaining an industrial CT image, and carrying out the preprocessing of the industrial CT image; s2) carrying out feature extraction on the preprocessed industrial CT image, and determining an area containing grid features in the industrial CT image; s3) performing grid division on the industrial CT image; s4) carrying out local encryption on the grid containing the grid feature part; and S5) generating a boundary unit grid by using a shortest distance method to obtain a local encrypted grid of the workpiece. According to the method, the space complexity and the calculation time in the grid generation process are reduced, the generated grid can adaptively realize local refinement in a workpiece curvature feature obvious region, and the problem that features are difficult to maintain in a direct generation method is solved.

The invention discloses a training method and device for a federal learning decision tree model, and relates to the technical field of big data. A specific embodiment of the method comprises the following steps: acquiring first credit characteristic data and a credit label of each user in a plurality of users; determining a first dissimilarity matrix according to the first credit feature data; receiving a second dissimilarity matrix sent by the data provider; determining global perception sample selection probability distribution according to the first dissimilarity matrix and the second dissimilarity matrix; and then cyclically executing the following processes: training to obtain a decision tree model: determining a user core set of the current node according to global perception sample selection probability distribution, then determining each sub-node after the current node is split according to the user core set, and taking each sub-node as the current node. According to the embodiment, the accuracy of model prediction can be improved, the performance of the model is improved, the optimization efficiency of the model and the training speed of the model are improved, and the training cost of the model is reduced.

The present invention relates to the technical field of sequence recommendation, and provides a sequence recommendation method and system based on a semantic generalization diffusion model, including: obtaining a user's behavior sequence; extracting the user's prior semantic behavior from an item-semetic dependency matrix based on the behavior sequence; statistically analyzing the occurrence frequency of each semantic behavior based on the prior semantic behavior to obtain a statistical sequence, and combining the time interval reweighting strategy to obtain the user's original interest distribution for the semantics; performing a reverse conversion on the user's original interest distribution for the semantics to obtain a generalized interest distribution; and predicting and obtaining a recommended item based on the generalized interest distribution. The space and time complexity of DM is reduced.

The application discloses a kind of relative radiation calibration methods and systems of remote sensing image based on quantile, it is related to remote sensing image processing technical field, the method includes: collecting satellite historical image data and separating according to spectral band;The value of each column of single-band image detector is sequentially arranged in ascending order, and quantile value is uniformly extracted to generate the quantile feature sequence of each column;The arithmetic mean value of all columns under the same quantile level is calculated, and the global reference quantile distribution is constructed;Based on least square method, the linear mapping relationship between each column quantile sequence and reference distribution is constructed, and the radiation correction gain and bias coefficient of each detector column are obtained;The original image is corrected column by column using the coefficient;The present application effectively eliminates the vertical strip noise caused by the non-uniformity of sensor response, overcomes the limitations of traditional calibration under the meteorological conditions and platform maneuvering, significantly improves the timeliness and image radiation quality of high-frequency and high-precision calibration.

The invention discloses a high-precision radar target recognition system based on orthogonal waveform design and a convolutional neural network. The high-precision radar target recognition system comprises a radar orthogonal waveform design module, a waveform generation module, a transmitting antenna module, a receiving antenna module, a waveform receiving module and a convolutional neural network module, by designing an orthogonal radar transmitting pulse string, the receiving entropy is increased, so that a radar echo pulse string received by a receiving antenna contains enough target information, and strong difference is presented between a target and clutter as well as multipath interference characteristics; therefore, the convolutional neural network can extract target key features from a radar echo pulse string received by an antenna and suppress clutter and multipath interference at the same time. According to the method, radar echoes are directly processed in the electromagnetic original domain, features are efficiently extracted, high-precision recognition is achieved, traditional multi-step signal processing is integrated into one step, the process is simplified, the hardware complexity of a receiver can be reduced possibly, and the method has great significance in improving the performance of an electronic information system.

The present invention discloses a continuous extremely fast packet receiving and unpacking method and communication equipment for splicing broken packets with a single buffer, and provides a general unpacking and broken packet splicing method applicable to "any protocol message with a packet start identifier field in the packet header". The present invention uses only a single thread and a single receiving buffer, does not open up any auxiliary space, and reduces the time overhead of the packet starting point positioning process by searching for the starting identifier of each packet in the area to be unpacked through the KMP string matching algorithm, reduces the scope of the pre-unpacking cross-boundary check of whether the remaining part is long enough for a packet by dividing the possible broken packet area, quickly identifies the broken packet by the cross-boundary check in this possible broken packet area, and quickly splices the broken packet by transporting the broken packet with a length of N to the buffer first address buf for storage and using buf+N as the cache starting point when receiving the next round of packets. The present invention meets the communication industry's requirements for low time and space complexity, versatility, etc. for unpacking, and overcomes the communication unpacking technical difficulties caused by sticky packets, broken packets, etc.

The present invention discloses a method, system and device for underwater image target detection, including: acquiring an underwater image, preprocessing the underwater image; inputting the preprocessed underwater image into a feature extraction network for feature extraction to obtain underwater image features; sending the preprocessed underwater image into a detection model to obtain a mask of the target in the underwater image; inputting the underwater image features and the mask of the target in the underwater image into a pre-trained generative adversarial model to obtain a pseudo underwater image; sending the pseudo underwater image into the detection model to obtain the position information, mask and category of the target in the underwater image to complete target detection. The present invention can realize underwater image target detection.

The present invention discloses a method for generating a full coverage path for an autonomously moving intelligent cleaning robot. The method mainly comprises the following steps: obtaining a global static map of an environment to be cleaned and geometric shape features of an area to be cleaned specified by a user on the static map, wherein the geometric shape features of the specified area correspond to the boundary of the area to be cleaned, and converting the map information required for coverage path planning into polygonal geometric feature information containing holes required by an algorithm by extracting boundary information of the cleaning area and boundary information of obstacles inside the area, decomposing the polygon to obtain a passable area without obstacles, generating a full coverage sub-path inside each passable area, generating a connection path connecting each sub-path through a visual graph algorithm between each sub-path, thereby obtaining a coverage path inside the entire specified cleaning area. The method has the advantages of high coverage rate, low memory usage during program operation, high computational efficiency, and the like.

The invention belongs to the field of central heating, relates to a data analysis technology, and aims to solve the problem of poor learning ability of a traditional method for spatial features of influence factors of secondary side water supply temperature of a heating station, in particular to a method for establishing a secondary side temperature supply time sequence model of a heating station of a central heating system. Comprising the following steps: acquiring historical secondary side temperature supply characteristics, meteorological characteristics and date characteristics which influence the secondary side water supply temperature of a heating station of a heat supply system; calculating the correlation between the meteorological data and the secondary side water supply temperature of the heating station; according to the method, meteorological characteristics strongly related to the secondary side water supply temperature of the heating station are determined, and a secondary side temperature supply time sequence data set of the heating station is constructed in combination with historical secondary side temperature supply characteristics and date characteristics; the influence of weather and holiday factors on secondary side temperature supply of the heating station can be considered, the prediction precision of a subsequent secondary side temperature supply time sequence model of the heating station can be improved by selecting proper features, and the time complexity and the space complexity of the model can be reduced.

The application discloses a strip ridge regression input optimization method and device, equipment, medium and product, and relates to the technical field of machine learning and data processing. The method comprises the following steps: performing feature extraction on obtained target stimulation data to obtain a plurality of original feature space data; constructing a kernel representation matrix for each original feature space data, and performing normalization processing on each kernel representation matrix; calculating similarity according to the normalized kernel matrices of each pair of original feature space data, and converting the similarity into a distance matrix between each pair of original feature space data; performing hierarchical clustering according to the distance matrix between all pairs of original feature space data to obtain a plurality of cluster-level feature spaces corresponding to feature clusters; jointly modeling based on the cluster-level feature spaces corresponding to all feature clusters and target response data; and solving a strip ridge regression input optimization model obtained by modeling to obtain a strip ridge regression input optimization result. The application can improve the calculation efficiency, prediction performance and interpretability.

The embodiments of the present application propose a method, device and electronic device for detecting junk software, the method comprising: extracting features to be detected from the software to be detected; detecting the features to be detected using preset rules to obtain a first detection result; inputting the features to be detected into a trained classification prediction model for detection to obtain a second detection result, wherein the trained classification prediction model is obtained by combining and training multiple features extracted by a logistic regression model LR feature extractor, a domain decomposition machine model FFM feature extractor and a deep neural network model DNN feature extractor; obtaining a final detection result based on the first detection result and the second detection result. The above method improves detection efficiency without significantly increasing time consumption and training costs.

The invention provides an analysis method of a digital logic circuit, computer equipment and a storage medium. The method comprises the following steps: acquiring a digital logic circuit to be analyzed; performing circuit grading by taking a trigger of the digital logic circuit as a node, and selecting the digital logic circuit between any two adjacent stages as a target circuit; wherein the target circuit comprises at least one path, and the at least one path comprises at least one combinational logic unit; determining a variable sequence of the target circuit, and based on the variable sequence, determining a first result of the at least one combinational logic unit according to a Boolean function; wherein the first result corresponds to Boolean difference of output to input of at least one combinational logic unit; determining a target path in the at least one path, and performing ITE operation according to a first result corresponding to the target path; and determining an identification result of the target path according to the operation result.

The present invention discloses a graph-based multiple sequence alignment method, system, device and computer-readable storage medium, the method comprising: obtaining a set of pan-genome sequence data, mapping the pan-genome sequence data, and obtaining a pan-genome colored graph; marking and obtaining the characteristics of the access status of a single node of the colored graph, traversing the colored graph to obtain a cSupB data model after decomposing the colored graph; obtaining the cSupB data model after the colored graph is decomposed, extracting the offset value data characteristics of the nodes in the cSupB data model; performing a first preprocessing on the cSupB data model based on the offset value data characteristics, and obtaining the initial alignment result of the cSupB data model. The present invention overcomes the problem that the traditional multiple sequence alignment method cannot meet the current situation when facing a large number of sequenced sample genomes.

The invention discloses a method and system for planning the shortest motion path of a mechanical arm of an industrial robot, and the method comprises the steps: building a rectangular plane coordinate system with the working range of the mechanical arm of the industrial robot as a reference; the coordinate position of each working point in the working range of the industrial robot mechanical arm is obtained; based on the coordinate position of each working point, calculating the distance between the working points; generating a complete graph data structure with a weight based on the distance between the working points, and constructing an adjacent matrix representing the complete graph data structure; based on the adjacent matrix, generating a distance matrix and a working point sequence matrix through a Floyd algorithm; and generating a shortest motion path between any two working points based on the distance matrix and the working point sequence matrix. The motion path of the industrial robot mechanical arm can be reasonably planned, loss and abrasion of the industrial robot mechanical arm are reduced, and meanwhile the structure is simple, computing resource consumption is small, and space complexity is low.

The present invention belongs to the field of centralized heating, relates to data analysis technology, and is used to solve the problem that traditional methods have poor spatial feature learning ability of factors affecting the secondary side water supply temperature of a thermal power station. Specifically, it is a method for establishing a secondary side heating time series model of a thermal power station in a centralized heating system, and comprises the following steps: obtaining historical secondary side heating characteristics, meteorological characteristics and date characteristics that affect the secondary side water supply temperature of a thermal power station in a heating system; calculating the correlation between meteorological data and the secondary side water supply temperature of the thermal power station; the present invention determines meteorological characteristics that are strongly correlated with the secondary side water supply temperature of the thermal power station, and constructs a secondary side heating time series data set of the thermal power station in combination with historical secondary side heating characteristics and date characteristics; it can take into account that the secondary side heating of the thermal power station is affected by meteorological and holiday factors, and selecting appropriate characteristics can increase the prediction accuracy of a subsequent secondary side heating time series model of the thermal power station, and can reduce the time complexity and space complexity of the model.

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 present invention discloses a deep learning decoding method for LDPC codes based on a hypernetwork. At present, the learning weights of channel neural decoding are trained for a specific channel environment, which makes it difficult to adapt to changes in the channel environment. In addition, the number of network layers of the channel decoder is difficult to flexibly change according to the application environment. In view of these two existing problems, a new channel neural decoder based on a hypernetwork is proposed. The decoder is composed of a hypernetwork and an NNMS network, wherein the NNMS network is the main network for channel neural decoding, and the hypernetwork generates learning weights for the main network; the hypernetwork is composed of a dense neural network, and its input and the input of the main network are both log-likelihood ratio LLR value vectors received by the channel. Experimental results show that compared with the existing channel neural decoding algorithm, the proposed decoding algorithm based on the hypernetwork can achieve better performance on LDPC, BCH and Hamming codes. In addition, the proposed framework can provide adaptability of learning weights and flexibility of network structure.

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.