35 results about "Initial sequence" patented technology

System receives data stream, groups sequential data segments associated with data stream until initial sequence of data segments is formed which is larger than minimum size. System groups sequential data segments which are next after initial sequence of data segments until next sequence of data segments is formed which combined with initial sequence of data segments is larger than maximum size. System determines feature value for each data segment in next sequence of data segments. System selects value from feature values, and data segment corresponding to selected value. System divides next sequence of data segments at selected data segment into part of initial group of data segments and part of next group of data segments. System combines part of initial group of data segments with initial sequence of data segments as initial group of data segments. System routes initial group of data segments or group of corresponding fingerprints for deduplication.

A quantum source coding method includes: initializing a quantum register having a plurality of nodes: loading a unary-coded message into the quantum register; loading an address state into an address register of each message node of the quantum register; sorting the message nodes based on a data register such that the message nodes form a message-sorted sequence; and applying, for each message node at an end of the message-sorted sequence, a CNOT gate to the address state of each of the register's message nodes and output nodes. The method also includes: unsorting the message nodes; sorting the nodes based on the address register such that the nodes form a fully-sorted sequence; applying, for each pair of adjacent nodes in the fully-sorted sequence having the same address state, a CNOT gate to the data registers of each pair; and unsorting the nodes to return the fully-sorted sequence to the initial sequence.

The invention relates to a parameter determination method, a data transmission method and device, a communication system and a medium. The method comprises the following steps: configuring a plurality of candidate parameter values corresponding to parameters of a receiving module in the serial-parallel converter according to an initial sequence signal sent by signal sending equipment; for each parameter, generating a first sequence signal when the parameter is configured as each candidate parameter value, and acquiring a second sequence signal when the parameter generated by the signal sending equipment is configured as each candidate parameter value; and according to the first sequence signal and the second sequence signal, determining a signal transmission error rate when the parameter is configured as each candidate parameter value, and determining the candidate parameter value with the minimum signal transmission error rate as an optimal parameter value of the parameter. By adopting the method, the optimal parameter value can be configured for each parameter in the RX module of the SerDes system.

In some examples, a proxy device tracks a count of how many half-open Transmission Control Protocol (TCP) flows are present in the proxy device. Based on the count breaching a threshold, the proxy device triggers activation, at the proxy device, of SYN cookie protection from an inactive state, where the SYN cookie protection includes generating an initial sequence number for a SYN-ACK packet based on applying a function on TCP state information associated with a received SYN packet from a client device. The SYN packet is to initiate a TCP flow between the client device and a server device, and the SYN-ACK packet is sent by the proxy device to the client device in response to the SYN packet.

Reduced complexity maximum likelihood sequence detectors (rMLSDs) for detecting multi-bit symbols, such as symbols found in pulse amplitude modulation (PAM), quadrature amplitude modulation (QAM), and phase shift keying (PSK) signal constellations with more than two constellation points, are disclosed. One illustrative digital communication receiver includes an initial equalizer that derives an initial sequence of symbol decisions from a filtered received signal, each symbol decision in the initial sequence having a second possible symbol decision, and an rMLSD that derives a final sequence of symbol decisions by evaluating a state metric for only each symbol decision in the initial sequence and its second possible symbol decision.

The embodiment of the invention discloses a method for determining a packet loss rate, network equipment, a storage medium and a program product, and belongs to the technical field of communication. In the method, first network equipment serving as a detection end sends a TCP synchronization request message to second network equipment serving as a destination end, and the TCP synchronization request message comprises an initial serial number, so that TCP connection with the second network equipment is established through the TCP synchronization request message. After the TCP connection is established, the first network equipment sends a plurality of detection messages to the second network equipment in sequence, and the serial number in the first sent detection message is not equal to the sum of the initial serial number and 1, so that the second network equipment can be stimulated to reply a response message comprising an SACK option for each detection message. And the SACK option in the response message can indicate the serial number range of the discontinuous data segment received by the second network equipment, so that the first network equipment can accurately determine the one-way link packet loss rate through the received response message.

This invention provides a time series data cleaning method and apparatus. The method includes: acquiring initial sequence data of an initial dataset, the initial sequence data including trend sequence data, periodic sequence data, and residual sequence data; performing a periodic component rationality judgment based on the initial sequence data to obtain target residual sequence data; performing anomaly detection on the target residual sequence data to obtain an outlier index of the initial dataset and performing an outlier removal operation to obtain the target dataset; and performing missing value imputation on the target dataset to complete the initial dataset cleaning. This invention uses a periodic component rationality judgment to analyze whether the periodic components of the sequence data are accurate and optimizes the residual sequence data. This significantly reduces the complexity of time series data while improving the accuracy of outlier identification during data cleaning, thus improving the accuracy, efficiency, and reliability of data cleaning, resulting in cleaner, more complete, and usable data after cleaning.

An artificial intelligence (AI) model is trained to generate a sequence of tokens, beginning with an initial sequence. Each sequence comprises a fixed number n of elements selected from a vocabulary list accessible to the AI model. A current sequence is iteratively transformed into a next sequence by adding a new element at a designated position and removing an element from another position to maintain the fixed number n of elements. The probability of selecting the new element for the next sequence is determined based solely on the current sequence, without dependence on sequences occurring before it. The sequence of tokens is iteratively output, starting with the initial sequence, using the iterative transformations of the current sequence to form the next sequence.

The invention discloses a beacon frame broadcasting method, a beacon frame broadcasting device, a beacon frame receiving method and a beacon frame receiving device. The method comprises the following steps: obtaining association identifiers of a plurality of sites and cache information, the cache information being used for describing whether data of a corresponding site is cached at an access point; based on the cache information, obtaining a target association identifier of a target station which caches the corresponding station data at the access point; an initial sequence is determined, and the number of bits included in the initial sequence is smaller than the number of bits included in a sequence needed when the multiple stations are independently mapped; assigning a plurality of bits in the initial sequence to which the target association identifier is mapped to obtain a target sequence; and packaging the target sequence as a virtual bitmap into a traffic indication diagram, packaging the traffic indication diagram into a beacon frame, and broadcasting the beacon frame. According to the invention, the technical problem of low beacon frame transmission efficiency is solved.

Provided in the present application are a communication method and apparatus, and a storage medium, which can increase the success rate of downlink synchronization for a terminal device in a deep fading environment, increase the probability of the terminal device receiving downlink paging, and can be applied to satellite communication systems, such as an NTN. The method comprises: a network device generating a first sequence, and sending the first sequence to a terminal device, wherein the first sequence comprises q gold sequences that are connected in series, and each of the q gold sequences is obtained on the basis of an initial sequence and a scrambling code sequence, the initial sequence being an m-sequence generated on the basis of one of q information bits, and the scrambling code sequence being an m-sequence generated on the basis of all or part of information bits from among first information bits having a length of z, which first information bits comprise the q information bits, and are used for indicating time-frequency resource configurations of subsequent sequences or information, and the number of repetitions, z and q being integers greater than 1, and q being less than or equal to z.

This invention relates to a method, apparatus, and medium for parallel scheduling optimization of drones and vehicles. The method includes: Step S1: Creating a set of demand points, etc.; Step S2: Sort all demand points in the demand point set and add a first symbol to the end of the initial sequence to obtain an initial node sequence; Step S3: Based on drone range constraints, etc., traverse and filter to obtain ultra-far points, etc., and move them after the first symbol to obtain an encoded sequence corresponding to the initial node sequence; Step S4: Decode the encoded sequence to obtain a decoded sequence; Step S5: Calculate the drone completion time and vehicle completion time of the decoded sequence, and further obtain the corresponding total completion time; Step S6: Apply random perturbation to the encoded sequence, and repeat steps S4 and S5, and take the encoded sequence with the minimum total completion time as the optimization result. Compared with the prior art, this invention can optimize short-term, high-burst, and short-distance scattered delivery scenarios.

The invention relates to a generative power line noise time domain modeling method fusing seasonal trends. The generator converts a random variable into an initial sequence with a low time resolution, and introduces a learnable position code to carry out time sequence learning; decomposing the time sequence characteristics through a season trend module, and dividing the time sequence characteristics into long-term change components and short-term fluctuation for modeling; and then with residual convolution as a core, a multi-stage up-sampling structure is adopted to gradually recover time resolution, and stable fusion of features and structural lightweight are realized. A discriminator adopts a multi-layer one-dimensional convolution structure, and comprehensive judgment on the authenticity of the whole sequence is obtained through small convolution kernel output features and global averaging. Meanwhile, in the training process, gradient penalty and frequency domain consistency limitation are introduced, a discriminator is restrained to meet the smooth continuity condition, and the model robustness is improved. According to the scheme, the problems that an existing model network is huge in structure, high in parameter quantity and difficult to deploy in embedded equipment can be solved.

The invention relates to a cable temperature prediction method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring actual operation data of a target cable in a preset time period; performing preprocessing to obtain processed actual operation data; constructing a training set and a test set according to a time sequence based on the processed actual operation data; constructing a long-time sequence prediction model, and training the long-time sequence prediction model based on the training set and the test set to obtain a trained long-time sequence prediction model; and inputting the processed actual operation data into the encoder module, constructing multi-scale time characteristics, inputting the multi-scale time characteristics into the decoder module, fusing the initial sequence of the liquid nitrogen temperature in the processed actual operation data, and predicting to obtain a liquid nitrogen inlet temperature sequence and a liquid nitrogen outlet temperature sequence in a preset time period. By adopting the method, the accuracy of cable temperature prediction can be improved.

The invention relates to a request identifier determination method and device for an input / output request, computer equipment, a storage medium and a program product, and the method comprises the steps: determining data storage position information of the input / output request when the input / output request is received, and obtaining a current sequence number of the data storage position information; data storage position information and a current sequence number of the data storage position information are stored in the memory; if the current sequence number fails to be obtained, generating an initial sequence number of the data storage position information, sending the data storage position information and the initial sequence number to a memory for storage, and determining a request identifier of the input / output request based on the data storage position information and the initial sequence number; if the current sequence number is successfully obtained, determining a target sequence number of the data storage position information through the current sequence number and a preset matching sequence number, and determining a request identifier based on the data storage position information of the input / output request and the target sequence number; the mode has no constraint on a protocol version and has no requirement on external conditions such as time precision.

This invention provides a method for identifying abnormal users, comprising: encoding various behavioral events of a target user on a live streaming platform to obtain a behavioral encoding sequence; converting the behavioral encoding sequence into an initial sequence vector matrix; determining a risk sequence vector matrix of the initial sequence vector matrix; determining a target sequence representation matrix based on the position offset vector matrix and time offset vector matrix of the initial sequence vector matrix and the risk sequence vector matrix; and performing anomaly identification on the target user based on the target sequence representation matrix, a preset normal label sequence representation matrix, and a suspected label sequence representation matrix. Thus, by first encoding and vectorizing the behavioral events to form the initial sequence vector matrix, and then adding risk labels to the risk sequence vector matrix, the method simultaneously considers the normal label sequence representation matrix, the suspected label sequence representation matrix, and the behavioral event occurrence patterns of abnormal users during anomaly identification, thereby further improving identification accuracy.

This application discloses a malware detection method, device, and computer-readable storage medium. The method uses a detection model based on a detection dataset of the target software to detect malware. The detection model can preprocess the interface instruction function sequences corresponding to multiple target software programs to obtain initial sequence features corresponding to the target software. This not only reduces the amount of data processing and improves detection efficiency, but also extracts features of the target software more deeply from different dimensions through different encoders and feature processors. Compared with traditional manual feature extraction and machine learning methods, this reduces human bias and improves the accuracy of identification.

The invention relates to a non-intrusive log compression optimization method and a non-intrusive log compression optimization system, which are characterized in that a field sequence adaptive to an existing compression algorithm is generated through an automatic means by taking representative data sampling, entropy driving to generate an initial sequence, hierarchical searching for an optimal sequence and parallel data rewriting as a core process; the compression efficiency is improved on the premise that an existing compression process is not changed, an existing compression / decompression process does not need to be modified, only a field rearrangement step needs to be added before data is written into and stored, the method can be directly integrated to existing data processing and adapt to various structural data, the calculation complexity is reduced through sampling evaluation and grouping search, and the method is suitable for large-scale popularization and application. The method supports configuration of core parameters (such as packet size and iteration depth), realizes flexible adjustment of compression ratio-compression speed, and has lightweight data protection capability.

This application discloses a data transmission method, apparatus, device, and medium, relating to the field of energy storage technology and applied to a battery cluster management unit. The method includes: responding to a data read request sent by a battery array management unit and determining a data transmission threshold corresponding to the current state of charge and current value of the battery cluster; determining whether the current battery data of each individual cell in the battery cluster and the historical battery data previously transmitted to the battery array management unit meet the transmission conditions set based on the data transmission threshold; if so, preprocessing the battery data between adjacent individual cells in an initial sequence based on the current battery data to obtain a processed sequence; compressing and encoding the processed sequence using a preset lossless compression algorithm to obtain compressed data, and transmitting the compressed data to the battery array management unit. This application can improve data transmission efficiency and avoid the transmission of invalid data and the unreliability caused by the loss of historical data.

The application discloses a power grid service mobile work order cooperative management system, relates to the technical field of work order cooperative management, and comprises an initial sequence generation module, a work order segmentation module, an initial relay chain construction module, an execution chain conflict adjustment module, a stage execution handover module, a deviation detection and exception identification module, an exception processing and updating module, a scheduling data recording module and a scheduling self-adaptive adjustment module.

The application discloses a ring-shaped shuttle task scheduling method and device, computer equipment and a storage medium. The method comprises the following steps: collecting real-time running data of a ring-shaped shuttle line to generate a system state snapshot; performing multi-round random pairing on an idle trolley and a to-be-transported cargo to generate n groups of initial matching sequences; randomly selecting two groups to perform fragment interception and mixed recombination to form new sequences until all the initial sequences are updated into new matching sequences; calculating the total running time of each group of new matching sequences; sorting the matching sequences according to the total running time, selecting the first n groups and randomly extracting part of them for mixed recombination again; determining the optimal n groups of matching sequences; and selecting a group with the shortest total running time to issue to a corresponding ring-shaped shuttle vehicle to execute a scheduling task. The method can improve the running efficiency and logistics processing capacity of the ring-shaped shuttle system, and provides an advanced technical solution for a logistics system in an intelligent manufacturing scene.

Trackable activity performed by a process executing in an operating system of a computing device is detected, the process associated with an initial sequence number and an initial message queue of a plurality of message queues, and each of the plurality of message queues comprising a first counter. Based on a comparison of the first counter to the initial sequence number, an assigned message queue of the process is set to the initial message queue or a second message queue of the plurality of message queues. A message is transmitted on the assigned message queue, the message comprising a process identifier of the process.

The invention provides a communication method, a communication device and a storage medium, which can improve the success rate of downlink synchronization of terminal equipment in a deep attenuation environment, improve the probability that the terminal equipment receives downlink paging, and can be applied to a satellite communication system, such as an NTN (Network Temporary Network). The method comprises the following steps: a network device generates a first sequence and sends the first sequence to a terminal device; the first sequence comprises q gold sequences which are connected in series, each gold sequence in the q gold sequences is obtained according to an initial sequence and a scrambling code sequence, the initial sequence is an m sequence generated based on one information bit in q information bits, the scrambling code sequence is an m sequence generated based on all or part of information bits in first information bits with the length of z, the first information bits comprise q blocks of information bits, the first information bits are used for indicating time-frequency resource configuration and repetition times of subsequent sequences or information, z and q are integers greater than 1, and q is less than or equal to z.

The invention provides a data transmission method and device based on an APHY protocol, equipment and a medium, and the method comprises the steps: sending an initial sequence to a first terminal, so as to enable the first terminal to return a first signal; receiving a first signal, and sending a white noise sequence in a preset format to the first terminal so as to complete link establishment based on an APHY protocol with the first terminal; and performing data transmission with the first terminal based on the link. By adding the white noise sequence in the preset format, the time of scrambling code locking and K sequence locking is shortened, and the data transmission efficiency is improved.

The application discloses a model hyperparameter determination method and device, electronic equipment and medium. The method comprises the following steps: obtaining an initial sequence model and a target large model constructed in advance; searching a hyperparameter space of the target large model through a specified optimization algorithm, so as to construct a label data sequence based on the hyperparameter space; performing supervised learning training on the initial sequence model through the label data sequence, to obtain a target sequence model; and determining a target hyperparameter of the target large model through the target sequence model. Thus, the label data sequence including the hyperparameter label is obtained through the specified optimization algorithm, and the initial sequence model is supervised and learned through the label data sequence, so that the initial sequence model learns the hyperparameter recommendation strategy of the specified optimization algorithm, and the target sequence model can be directly used to quickly and accurately determine the hyperparameter of the target large model.

The invention relates to a data prediction method and device, electronic equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring historical sequence data of a to-be-predicted object; the sequence length of the historical sequence data is matched with the target sequence length; the target sequence length is obtained by performing adaptive iterative updating on a plurality of initial sequence lengths; inputting the historical sequence data into a pre-trained data prediction model for multi-step prediction processing to obtain prediction data of the to-be-predicted object in a plurality of continuous time steps in the future; the pre-trained data prediction model is obtained by training sample sequence data of the to-be-predicted object; the sequence length of the sample sequence data is matched with the target sequence length; and obtaining a data prediction result of the to-be-predicted object based on the prediction data of the to-be-predicted object in the plurality of continuous time steps in the future. By adopting the method, the data prediction accuracy can be improved.

The invention provides a pseudo-random sequence generation method, which comprises the following steps of: performing first recursive exclusive or operation on an initial sequence value in a first initial m sequence to obtain a first m sequence; performing second recursive exclusive or operation on the initial sequence value in the second initial m sequence to obtain a second m sequence; and generating a pseudo-random sequence according to the first m-sequence and the second m-sequence. The disclosure also provides an electronic device, a computer readable medium, and a computer program product.

The invention is suitable for the field of power load prediction, and discloses a load time sequence mixed quantum fusion prediction method, system, device and medium, and the method comprises the steps: obtaining multi-channel historical load time sequence data, carrying out the sequence embedding processing, obtaining an initial sequence, and inputting the initial sequence to a plurality of layers of star aggregation-distribution modules which are sequentially connected in series; wherein the star aggregation-distribution module adopts a first quantum parallel hybrid network to carry out network processing on an initial sequence to obtain a multi-sequence representation; performing random pooling operation through multi-sequence representation to obtain core representation; the core representation and the multi-sequence representation are spliced, a second quantum parallel network is adopted for fusion, and an output sequence is obtained; the output sequence of the previous module is used as the input of the next module, and the final output sequence is used as a power load prediction result. According to the method, the parallel mixed classical quantum regression network is introduced into the star aggregation-distribution module, so that complex time sequence data is effectively processed, and the time sequence prediction performance is improved.

The invention discloses a structured report generation method and system for clinical dialogue data, and the method comprises the steps: preprocessing the clinical dialogue data, reconstructing a relative time axis, and obtaining an initial sequence matrix with role marks and a normalized relative time set; a time sequence-medical entity is extracted in a combined mode, a medical event and a time anchor node set are obtained, and then a directed heterogeneous dynamic time sequence dependency graph containing co-occurrence association, time sequence smoothness and semantic causal edges is constructed; then encoding the graph through a graph attention network to extract graph encoding features; and finally, fusing graph coding and text coding features, and performing controlled decoding under structured constraints to generate a structured medical report. According to the method, the medical report logic accuracy and generation efficiency can be improved.

This invention relates to a statistical learning-based ViT model pruning method, pruning device, and image recognition method. The pruning method includes: using an initial token sequence as the current input token sequence; obtaining the current input token feature sequence through the current encoding layer of a Transformer encoder; determining the number of tokens to be retained in the current encoding layer according to a preset pruning ratio; obtaining the currently retained token feature sequence from the current input token feature sequence based on a statistical learning method, according to the current input token feature sequence and the number of tokens to be retained; and traversing all encoding layers of the Transformer model to obtain the final token features. The pruning method of this invention can reduce the possibility of erroneously deleting important tokens, thereby improving model performance and the accuracy of results for tasks such as image classification.

The present disclosure provides a pseudo-random sequence generation method. The method comprises: performing first recursive XOR operation on an initial sequence value in a first initial m-sequence to obtain a first m-sequence; performing second recursive XOR operation on an initial sequence value in a second initial m-sequence to obtain a second m-sequence; and generating a pseudo-random sequence on the basis of the first m-sequence and the second m-sequence. The present disclosure further provides an electronic device, a computer-readable storage medium, and a computer program product.