58 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.

Methods, systems, and devices for wireless communications are described. A device may determine a first quantity of first candidate sequences and a second quantity of second candidate sequences. Additionally, the device may obtain an initial index representing an initial sequence of bits to be communicated by the device, where the initial index is less than a product of the first quantity and the second quantity. Additionally, the device may extract a first index and a second index from the initial index, where the first index is less than the first quantity, the second index is less than the second quantity, and the initial index is a function of the first and second indices. The device may determine a first sequence based on the first index and a second sequence based on the second index, and may transmit a signal indicative of the first sequence and the second sequence.

The invention provides a fault prediction method and device, equipment, a storage medium and a program product, and the method comprises the steps: obtaining an initial sequence corresponding to a plurality of abnormal events of target equipment, and the initial sequence comprises the event information of each abnormal event; determining a plurality of target subsequences in the initial sequence, wherein the number of event information included in the target subsequences is smaller than or equal to a first threshold value; and determining a fault prediction result of the target equipment according to the plurality of target subsequences. And the accuracy of fault prediction on the target equipment is improved.

This application relates to a method, apparatus, computer device, computer-readable storage medium, and computer program product for generating a sequence number. The method comprises: generating sequence number rule configuration modules for a target service based on service feature information of the target service and an initial sequence number rule template; determining an initial target sequence number rule configuration module within each sequence number rule configuration module, and generating a target sequence number rule configuration module based on the initial target sequence number rule configuration module; the target sequence number rule configuration module is used to configure and generate a target service sequence number for the target service. This method can improve service sequence number query efficiency.

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.

The invention relates to the technical field of voice processing, can be applied to business scenes such as financial science and technology and medical health, and discloses a non-autoregressive optimized data sequence processing method, device, equipment and medium, comprising: receiving an input sequence code to generate a hidden representation, and determining a dynamic length span based on a time step value and the hidden representation, generating predicted values in parallel in the dynamic length span, identifying the leftmost position index and retaining the corresponding predicted value, judging whether the sequence is completed or not, repeatedly updating the time step value when the sequence is not completed so as to determine a new dynamic length span until the corresponding predicted value of the leftmost position index is retained, and accumulating the retained predicted values to form initial sequence data; and optimizing the initial sequence data by using a non-autoregression process to generate target sequence data. According to the method, time step dynamic updating and dynamic length span control are combined, and parallel prediction and non-autoregression optimization processes are matched, so that the speech sequence generation efficiency is improved, and meanwhile, the naturalness and coherence of the speech are enhanced.

The invention provides a computing unit full interconnection communication system and a message forwarding method. The computing unit full-interconnection communication system comprises N programmable switching chips and M computing units; the programmable switching chip supports a standard Ethernet communication protocol and a private communication protocol; the programmable switching chip is used for converting an initial serial number of an initial data message into a conversion serial number after receiving the initial data message sent by the source computing unit, and sending the conversion serial number to the target computing unit. The message forwarding method comprises the following steps: analyzing an initial data message sent by a source computing unit; determining a target port according to forwarding information in the message analysis result; obtaining a conversion serial number according to the target calculation unit, and replacing the initial serial number with the conversion serial number to obtain a conversion data message; and forwarding the converted data message to the target computing unit by using the target port. According to the method and the device, the compatibility of communication modes among the computing units and the flexibility during longitudinal expansion can be improved.

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 port multiplexing method and device, electronic equipment, a medium and a computer program product, and the method comprises the steps: obtaining a first serial number corresponding to a first communication session; the first serial number is the serial number of an FIN data packet corresponding to the first communication session; determining a second serial number corresponding to the first moment based on a preset relationship; the first moment is an initial moment of a to-be-determined retention duration of the first communication session; determining the retention time length based on the size relationship between the first serial number and the second serial number, and enabling the serial number obtained by progressive increase based on the second serial number to be greater than the first serial number after the retention time length; determining an initial serial number of a second communication session based on the first serial number under the condition that a multiplexing moment is within the retention duration; the multiplexing moment represents a moment when the second communication session needs to perform port multiplexing with the first communication session.

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.

The invention discloses an access synchronization sequence construction method, a detection method and a base station access method, and the access synchronization sequence construction method comprises the steps: constructing a first initial sequence set based on a plurality of first initial sequences satisfying low correlation, constructing a second initial sequence set based on a plurality of second initial sequences meeting low correlation; the low correlation meeting comprises that the sequence itself meets the low autocorrelation and the sequences meet the low cross correlation; and performing Kronecker product operation on any two initial sequences from the first initial sequence set and the second initial sequence set to obtain a plurality of target sequences, and constructing a set of access synchronization sequences distributed to a user terminal based on the target sequences. The requirement that large-scale users access the network at the same time can be met, and high detection performance and system reliability are guaranteed.

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 the technical field of personnel sorting, in particular to a dynamic personnel sorting method for a super-large-scale organization. The method comprises the following steps: generating a fixed 9-bit integer sequence code by monitoring the total number of corresponding personnel in an organization in real time and automatically starting a rule I and a rule II according to a preset threshold value; generating an initial sequence code according to the currently started coding rule, wherein the initial sequence code comprises a unit sequence number, a department sequence number and a personnel sequence number; generating a corresponding adjustment value by receiving an adjustment instruction executed by an administrator on the personnel sequence number, and injecting the adjustment value as a priority value into the tail section of the initial sequence code to generate an adjusted sequence code; updating the corresponding sequence code again by using an incremental updating algorithm so as to generate an updated sequence code; and the synchronous management of double-post sorting of personnel is realized by utilizing a main post / facultative post collaborative coding technology. According to the invention, efficient, accurate and dynamic sorting of personnel in a super-large-scale organization can be realized.

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 discloses a data link traceability detection method and system based on abnormal network behaviors, and the method comprises the steps: firstly extracting three features which are difficult to counterfeit at the same time, namely, TTL and TCP window sizes and TCP initial serial numbers, from network traffic, forming a sequence, and improving the reliability of a traceability result; analyzing the TTL value by using a clustering algorithm, and calculating the most possible initial network hop count of a data packet source; constructing a path topology model by fusing the hop count, the window size and the serial number sequence and adopting a particle swarm optimization algorithm and stability analysis; and then, on the basis of the path topology model, performing dual verification by combining graph analysis and variable coefficient statistical test, finally locking the specific position of an attack source in the network topology, and realizing accurate source tracing of the network attack.

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.

The application provides a sensor data sequence analysis reporting method. After a terminal is installed and powered on, initial sequence 1 and sequence 2 are formed by using sensor data collected within a certain time after power-on. When the terminal reaches a reporting period, sensor data collection is performed. Multiple sensor data samples are collected each time data is collected, and the collected samples are filtered to obtain a current period collection value. Sequence 1 is updated using the current period collection value, and sequence 2 is updated using sensor data collected between two period collections. Sequence 1 and sequence 2 are used as auxiliary judgment basis for device triggering. The application uses a method of analyzing and reporting by using sensor data sequence characteristics, thereby reducing the pressure of platform-side abnormal data analysis.

The present invention discloses a method, system, and device for generating a large-capacity mutually orthogonal complementary sequence set, belonging to the field of data processing. The method comprises: using a long short-term memory (LSTM) network for training to generate an initial sequence set; reshaping the initial sequence into a symmetric weight matrix through a Hopfield neural network and performing asynchronous state updates; grouping and cutting the updated sequence set to generate a pending sequence set; and evaluating the pending sequence set based on the MC indicator to screen and obtain a final mutually orthogonal complementary sequence set. By leveraging the LSTM's ability to model long-term sequence dependencies and combining iterative optimization of orthogonal complementary constraints with the Hopfield neural network, the present invention breaks through the number theory limitations of traditional algebraic constructions and can rapidly generate large-scale sequence sets that meet zero cross-correlation and zero autocorrelation sidelobe conditions in a data-driven manner.

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 application discloses a DNA storage clustering method for dynamically updating a hash index, comprising selecting DNA fragments at three positions of an initial sequence, converting the three DNA fragments into corresponding hash values through hash code calculation, and mapping the hash values into three hash tables. In a subsequent sequence to be clustered, DNA fragments are selected at the three positions, hash codes are calculated respectively, and collision detection is performed, so as to determine whether the sequence belongs to the same cluster. When the number of sequences in the cluster reaches 5, the core index set is updated once, so as to avoid the error index from leading to the error shunting of the sequence. The DNA storage clustering method for dynamically updating the hash index can significantly improve the similarity of the sequences in the cluster due to the timely updating of the index, and avoids the error shunting of the sequence caused by the error index. The method has the advantages of improving the accuracy and reliability of the clustering, and provides good support for large-scale DNA data storage.

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.