133results about How to "Reduce storage overhead" patented technology

The invention discloses a lightweight dynamic security authentication method and system based on PUF (Physical Unclonable Function), and the method comprises the steps: S1, a server side collects to-be-registered information, generates a random challenge and auxiliary information, receives a response corresponding to the challenge generated by an IOT (Internet of Things) device side through a PUF structure, and sends the response to the server side; storing the pseudonym and the private key of the registered user and the CRP pair of the user equipment; s2, when the server side receives the authentication request, the server side initializes the authentication request, generates a challenge of the next round of authentication by using the challenge of the current authentication in the initialization process, and sends the challenge of the next round of authentication to the Internet of Things equipment side; the server side verifies the identity of the device after receiving the data packet sent by the Internet of Things device side; and S3, updating a next round of pseudonym at the server side, generating a next round of challenge and CRP pair by the server side, and sending the challenge and CRP pair to the Internet of Things equipment side. The method has the advantages of being simple in implementation method, small in resource overhead, high in flexibility, capable of dealing with various security threats and the like.

The invention belongs to the technical field of model training, and particularly relates to a large model increment training method and system based on dynamic sparsification. The method comprises the following steps: S1, evaluating the parameter importance of a large model by adopting a multi-dimensional evaluation method, and reflecting the contribution of parameters to the performance of the large model; s2, performing dynamic sparsification control on the large model structure by adopting a self-adaptive structure adjustment method; s3, performing incremental training management on the large model by constructing a capacity-adjustable empirical playback buffer pool and proposing a multi-objective loss function; and S4, carrying out model compression optimization on the large model by adopting a mixed precision quantification strategy. The method has the characteristics that the network structure can be dynamically adjusted by evaluating the importance of model parameters in real time, so that the efficient training is realized while the model performance is ensured.

The invention relates to integrated airborne communication antenna equipment and a communication method. The method comprises the following steps: carrying out CSI data acquisition and space-time focus modulation on a communication channel to obtain CSI space-time characteristic data; performing residual vector quantization operation to generate a quantized data stream, and performing recursive coding processing to obtain a target data stream; mapping the target data stream to a three-dimensional tensor space, constructing a real value tensor model, and performing iterative operation to obtain a CSI feature vector; performing fractional order wavelet transform decomposition on the CSI feature vector to obtain a radio frequency feature set, and inputting the radio frequency feature set into a federal fractional scattering network for feature processing to obtain an identity authentication result; and calculating an amplitude-phase weight coefficient of the antenna array, generating a beam parameter, and performing compensation matrix operation on the beam parameter according to the CSI feature vector to obtain a pre-coded signal. According to the invention, the dynamic adjustment of the antenna beam parameters is realized, and the accuracy and anti-interference capability of beam forming are improved.

The invention relates to a symmetric searchable encryption method and device for big data based on an inverted dictionary, and the method comprises the steps: receiving file data and a target password uploaded by a user in a C / S distributed structure, and controlling a user manager to extract at least one keyword in plaintext data through employing a TF-IDF algorithm and Kmeans clustering combination method, so as to build the inverted dictionary; the method comprises the following steps: encrypting file data by using a preset AES algorithm to obtain encrypted file data, constructing encryption chain tables by using the preset AES algorithm and the like, adding an HMAC message verification code at the tail end of each encryption chain table, and performing noise filling on the encryption chain tables through a target password to generate a node sequence and an index sequence; and querying a file name corresponding to the target keyword through the target password by using the cloud server. Therefore, the problems that extra communication and storage expenses are easily increased, the response speed is low, and a data owner needs to be online for a long time in a single-write multi-read scene when a server performs inert calculation are solved.

The invention relates to an industrial wireless system millimeter wave beam prediction method assisted by multi-mode environment semantics, and belongs to the technical field of industrial wireless communication. According to the method, for a millimeter wave wireless communication process from a base station to a user in a factory, absolute position environment semantics of the user in a global positioning system and relative position environment semantics relative to the base station are extracted and obtained through a global positioning module deployed in the user and a camera of the base station; and inputting into a lightweight efficient neural network prediction model, and finally outputting a beam index to complete millimeter wave beam prediction. According to the method, the actual communication process of the millimeter wave communication system in the industrial wireless environment is considered, the efficient prediction of the beam is realized on the premise of low overhead of system resources, and the requirements of high-mobility and low-delay industrial wireless communication scenes are met.

The invention provides a multi-process-mode intelligent switching method and system for vehicle-mounted sludge treatment, and the method comprises the steps: carrying out the time-space alignment and standardization of multi-dimensional sensing data such as sludge characteristics, environmental parameters and equipment states, carrying out the real-time reasoning of a process switching critical value through combining with a lightweight incremental regression model, generating a self-adaptive threshold value band with a confidence interval, and carrying out the real-time reasoning of the process switching critical value. Dynamic monitoring and mode switching pre-judgment of actual operation parameters are realized; in the switching process, a weighted fusion progressive transition control strategy is adopted, the transfer rate is adaptively adjusted according to real-time feedback, the operation stability is guaranteed, and the intelligence, reliability and switching smoothness of process switching of the vehicle-mounted sludge treatment system are remarkably improved.

The embodiment of the invention discloses a harmonic compensation method for an angle error of a magnetic encoder, and the method comprises the steps: enabling the magnetic encoder to rotate at a constant speed in a setting mode, collecting sine and cosine signals outputted by the magnetic encoder, and carrying out the calculation through CORDIC, thereby obtaining an initial angle sequence containing a fundamental wave error and a harmonic wave error; extracting each harmonic component through an adaptive wave trap, constructing a harmonic compensation polynomial, and storing each coefficient; in an application mode, a magnetic encoder signal is collected in real time, a current initial angle value is solved, a harmonic compensation amount is calculated according to a current fundamental wave angular frequency and a stored coefficient, and an accurate angle value is obtained by subtracting the compensation amount from the initial angle value. According to the method, external reference signals and a large amount of storage space are not needed, each harmonic error is directly eliminated through a harmonic compensation polynomial, and approximate errors caused by traditional linear interpolation are avoided; and the compensation precision can be improved only by increasing polynomial terms, and the storage requirement is not obviously increased.

The invention discloses a cache request processing method, equipment and a medium, belongs to the technical field of data processing, and is used for solving the technical problem that in the prior art, after the size of a cache line is reduced, a system needs to process a cache request more frequently, and the complexity and overhead of cache management are increased. The method comprises the steps that n block requests are combined into a total request, and n is a positive integer; determining whether a target data block corresponding to any one request in the total request is cached and hit; if the cache of the target data block is hit, obtaining cache data corresponding to the target data block from a cache; and if the target data block cache is missed, sending a missed request to the L2 cache, distributing a target cache space for the target data block in the L1 cache, and obtaining corresponding cache data from the target cache space. According to the method, the size of the cache line is reduced, the frequency of processing the cache request is reduced, and the complexity and overhead of cache management are reduced.

The invention discloses a fine-grained access control method of an intelligent lighting system combining CP-ABE and a block chain, and the method comprises the following steps: initialization: carrying out the initialization of the CP-ABE through an attribute authorization mechanism, and generating a public key and a master key of the CP-ABE; key generation: the user generates a private key of the attribute of the identity by using the audited attribute of the identity, the public key and the master key, and a single user can have a plurality of different private keys; data encryption: formulating an access rule, and encrypting the data to generate an encrypted file of a file suffix bit CP-ABE after a data owner formulates the access rule; the user stores the chameleon hash value of the ciphertext file, the access control strategy of the ciphertext file and the path of the IPFS on the IPFS where the ciphertext file is located to block chain data; the method has the advantages of high safety, high accuracy and the like.

The invention relates to an index prefix compression processing method and device, computer equipment, a computer readable storage medium and a computer program product, and relates to the technical field of databases. According to the method, intelligent compression of the index prefix can be realized, the compression efficiency is improved, the space occupation is reduced, and the storage overhead of the index is reduced. The method comprises the following steps: generating a prefix symbol for simplifying and representing a corresponding repeated prefix part according to the repeated prefix part of each original index through a symbol generation model; the symbol generation model generates a prefix symbol corresponding to the original index based on the use condition of a historical index containing a historical prefix symbol and / or the use condition of an index sample containing a prefix symbol sample; registering each prefix symbol corresponding to each repeated prefix part in a prefix symbol table; and for the target index, replacing the repeated prefix part of the target index with the corresponding prefix symbol according to the prefix symbol registered in the prefix symbol table.

The invention belongs to the technical field of time sequence anomaly detection, and particularly relates to a streaming time sequence sub-sequence anomaly detection method fusing global and local features, which comprises the following steps: clustering all sub-sequences in an initial batch, constructing an inverted index, and performing approximate neighbor search on each sub-sequence; the used calculation formula is used for measuring deviation between the subsequences and neighbors of the subsequences and between the subsequences and cluster centers of clusters containing the neighbors of the subsequences and dispersity of neighbor distribution, and global and local feature fusion is achieved; taking each current cluster as a historical cluster, calculating the maximum distance between each sub-sequence in each historical cluster and the cluster center of the cluster, and taking the maximum distance as a density parameter of the historical cluster; and clustering all the subsequences in the latest batch, calculating the Euclidean distance between the cluster center of each new cluster and the cluster center of each historical cluster, carrying out cluster combination based on the comparison of the Euclidean distance and the density parameter of each historical cluster, and repeatedly executing neighbor search and abnormal score calculation. According to the invention, accurate detection of large-scale streaming data can be realized.

The invention relates to a data transaction security and privacy protection method based on a zero-knowledge proof smart contract. The method comprises the following steps: initializing block chain parameters; the seller and the purchaser register privacy identities with the credible supervision mechanism; a seller generates an ownership relationship of data to be sold and a zero-knowledge proof of a data ownership proof contract, links {alpha, # imgabs0 # T1, sign1}, and obtains an address on the link; the seller generates a zero-knowledge proof for preventing the machinability attack contract, verifies the zero-knowledge proof according to the uplink information, and releases information of the data to be sold after the zero-knowledge proof passes the verification; the method comprises the following steps: a purchaser requests purchase data from a seller, the purchaser generates a zero-knowledge proof of a data ownership privacy transfer proof contract according to a new ownership relationship calculated by the seller, verifies the zero-knowledge proof, obtains a ciphertext storage address of the purchase data, and links {alpha, # imgabs1 # T2, AddrMnew, add and sign2}. The method has better security and privacy characteristics.

The present invention discloses a method for efficiently caching historical data in an offline state of an Internet of Things data collection gateway, including a deployment relationship of the Internet of Things data collection gateway, which includes field instruments, the Internet of Things data collection gateway, and a cloud server. The data of the field instruments are collected by the Internet of Things data collection gateway and temporarily stored inside the Internet of Things data collection gateway, and the data is uploaded to the cloud server through communication methods such as network transmission; users do not need to care about the size of memory and disk space, the cache engine storage efficiency is not high, resulting in a small amount of cached data, and the disk will be full in a long-term offline state.

The invention discloses a privacy protection method for data sharing in VANETs based on a block chain and a reputation mechanism, relates to a privacy protection method for data sharing in VANETs, and belongs to the field of privacy protection. The invention provides a privacy protection method for data sharing in VANETs based on a block chain and a reputation mechanism in order to solve the problem that malicious users leak user privacy or inject damaged data and the like in the vehicle data sharing process in existing VANETs. The method comprises the following steps of: 1, respectively initializing a certificate authority (CA), a block chain member, a vehicle user and the reputation of the vehicle user; 2, performing data sharing based on an initialization result in the step 1; 3, reputation feedback is carried out based on data sharing in the step 2; and 4, vehicle reputation updating is carried out based on the reputation score obtained in the step 3.

This application relates to the field of monitoring data processing technology, and particularly to a method, device, and storage medium for preventing monitoring data from being tampered with. The method includes: during the recording of monitoring data, simultaneously acquiring a thermal noise segment from an imaging sensor for each video frame, reducing equipment costs and deployment difficulty; calculating the image activity metric of the video frame and the noise energy metric of the thermal noise segment, requiring minimal computation and meeting the simple real-time needs of ordinary monitoring equipment; performing an arithmetic combination of the image activity metric and the noise energy metric to obtain a frame verification feature value, which is then associated with and stored with the video frame, making it difficult to forge and having low storage overhead; acquiring the video frame to be verified and generating a verification feature value based on the video frame; determining that the monitoring data has been tampered with when the deviation between the verification feature value and the stored verification feature value exceeds a preset range, thus achieving low-cost, low-complexity, and high-real-time monitoring data integrity protection.

The application belongs to the technical field of network security, and discloses a quasi-state compiling method and device for an ARM single-chip microcomputer, which comprises the following steps: customizing a linking file required by a cross-compiling tool chain to complete memory layout of an executable program; for a master controller of an ARM architecture and a Cortex series kernel, based on a cross-compiling technology, using the linking file required by the cross-compiling tool chain, realizing a heterogeneous execution body with offset of a read-only data segment and a stack area loading base address under the architecture and the kernel through compiling and linking; the heterogeneous execution body processes external input data, if the external input data is a normal data packet, the outputs of the heterogeneous execution bodies are the same, and if the external input data includes an attack data packet, the outputs of the heterogeneous execution bodies are different. The application is guided by the quasi-state defense theory, and through construction of an address redundancy heterogeneous execution body with variable loading base address, the attack threat of a buffer vulnerability can be reduced.

The invention provides a multi-virtual power plant scheduling method based on cloud edge collaboration. The method comprises the following steps: 1) a dispatching master station of a power system cloud accesses a plurality of virtual power plants, and establishes unified management ledger data; (2) each virtual power plant respectively calculates the maximum value, the minimum value and the optimal solution of the adjustable load of the virtual power plant in the time period according to a demand index issued by the master station, and returns a result to the scheduling master station of the cloud; and 3) the scheduling master station performs unified fine adjustment calculation according to the results submitted by the virtual power plants, and issues the adjusted load results to the corresponding virtual power plants, and the virtual power plants perform power generation according to the issued load results and then supply power to the network. According to the application of the method, improvement is carried out for insufficient collaboration of a plurality of virtual power plants in an existing method, the situation that local power supply adequacy of a power distribution network is unbalanced is avoided, and distributed clean energy is preferentially consumed in the virtual power plants.

This invention discloses a method, apparatus, medium, and product for calculating bonding interactions in molecules. The method includes: allocating bonding tasks using a three-dimensional interconnected computing architecture, and constructing a bonding atom set and a bonding task index table matching the allocated bonding tasks within each computing module. This ensures that identical atoms do not need to be repeatedly stored when the computing module executes all bonding tasks, reducing the storage overhead of atomic information; dynamically acquiring and filling atomic information from the local machine or other computing modules into the corresponding storage locations of the bonding atom set, extracting atomic information according to the index table, calculating bonding components in parallel, and accumulating the bonding components of identical atoms to obtain the resultant bonding force; and updating the resultant bonding force and atomic information through inter-module interaction, and re-storing the atomic information according to the updated spatial arrangement. This reduces the amount of data transmission between modules while ensuring the accuracy of molecular bonding interaction simulation, thereby improving the stability and efficiency of the overall molecular dynamics simulation.

This disclosure provides a data processing method, apparatus, and computer device based on butterfly networks, relating to the fields of artificial intelligence and hardware acceleration technology. The solution is as follows: In a switchable data processing architecture including an inverse butterfly network, a preprocessing module, a post-processing module, and a control signal generation module, when switching to aggregation operation, the preprocessing module inputs the first input data into the inverse butterfly network, the control signal generation module outputs the first control signal to complete data processing, and the post-processing module directly outputs the aggregation result. When switching to distribution operation, the preprocessing module performs input position mapping and filling on the second input data to obtain the third input data, the control signal generation module outputs the adapted second control signal to complete data processing, and the post-processing module performs rearrangement on the intermediate results to output the distribution result. This reduces hardware resource consumption, lowers control complexity and processing latency, and improves the hardware deployment adaptability of neural network sparse computing while ensuring functional integrity.

The invention relates to a trusted security traffic monitoring system based on an anti-quantum key management framework, and relates to the technical field of smart city traffic, and the system comprises an initialization module which is used for carrying out the initialization processing of a cloud platform, an edge gateway and a camera node; the data acquisition and transmission module is used for acquiring and encrypting monitoring data based on the initialized camera node, and transmitting the encrypted monitoring data to the cloud platform through the edge gateway in a preset data transmission mode; wherein the preset data transmission mode comprises a cross-gateway area transmission mode and a same-gateway area transmission mode; and the data detection module is used for deleting or replacing the scene of the corresponding node when the edge gateway or the camera node shows a malicious behavior or the equipment is aged. According to the method, a key system is constructed through an encryption and decryption algorithm, so that the key management framework oriented to edge computing has quantum aggression resistance, and the security of a city monitoring system in a traditional scheme is improved.

The embodiment of the invention provides a lightweight lattice-based ring signature method suitable for a satellite network. The method is applied to the technical field of information security and comprises the steps that a ground control center generates system public parameters; the user generates a complete signature key pair based on the NTRU grid, and a compression verification identifier for the identity identifier is obtained through calculation of a complete signature public key; the ground control center constructs a gridding user set by using a space-time fragmentation technology, calculates Merkle tree roots as ring fingerprints and distributes the ring fingerprints to satellites; the user generates an anti-replay ring signature and member proof in combination with ephemeris information and spatio-temporal context; the satellite node executes lightweight rapid pre-verification according to the time, the position and the ring fingerprint; and the ground control center executes complete ring signature and membership verification. The method effectively solves the contradiction between limited satellite network resources and high-security requirements, and has the capability of resisting quantum computing attack.

The application discloses a blockchain-based ticket settlement verification method and system, relates to the technical field of financial transaction safety, and comprises the following steps: S1, receiving a cross-border order submitted by a buyer, verifying the order integrity and the authenticity of the buyer's identity by using a digital signature algorithm, analyzing and extracting the buyer's information, the HS code of the commodity, the payment amount and the currency, and completing the consistency comparison of the ticket basis information in combination with the corresponding tax payment price interval of the HS code; S2, calling an off-chain risk control engine to carry out multi-dimensional compliance verification in parallel, generating a comprehensive risk score and dividing a risk level; after the verification, a preset aggregation template is matched based on the risk level, a unique feature identifier of the order is generated by using a hash algorithm; the risk level is adaptively matched with batch aggregation parameters and on-chain priority, and differentiated dynamic scheduling of off-chain batch evidence is realized. The application realizes full-dimensional pre-checking to avoid false transaction risks, and builds a risk level adaptive adjustment mechanism to balance business processing efficiency and risk control safety.

The application discloses a student programming answer prediction method combining semantic understanding and structured modeling, collects student programming homework data and pre-processes the data to obtain data samples, translates the data samples to obtain an English feature set; uses low-rank adaptation to fine-tune a pre-trained semantic understanding model, constructs input features, and predicts an answer correctness probability; performs graph neural network structured modeling based on the association relationship between problems and concepts, performs message propagation and feature updating to obtain an embedding set, inputs the embedding set and student embedding into a prediction layer to obtain a probability of answering a question correctly; and inputs the probability of answering a question correctly and the probability of answering a question correctly after fusion into a multilayer perceptron for nonlinear mapping to predict the probability of answering a question correctly. The application solves the problems of difficulty in effectively processing non-standardized codes submitted by students, insufficient semantic understanding of question texts and low prediction accuracy in programming knowledge tracking, and provides a scientific basis for personalized teaching and learning resource scheduling.

The present invention discloses a threat detection method, device and equipment based on an APT attack graph. The method of the present invention includes the steps of APT attack graph generation, intrusion alarm collection, attack path identification, APT attack detection, and evidence chain reconstruction. The present invention reduces the real-time attack analysis overhead by designing an APT attack graph and pre-analyzing attack actions according to network and system information; evaluates attacks from multiple angles such as harmfulness and necessity to ensure the comprehensiveness of threat detection, and designs an attack path scoring method to achieve global attack analysis; associates attack behavior traces to form an evidence chain, and uses a gray list to associate multiple attack entities to achieve accurate positioning of the attack impact range. By combining the three capabilities of APT attack prediction, detection and monitoring, the problem of threat alarm fatigue is effectively combated, and the robustness of zero-day vulnerability exploit detection is improved, so that the present invention can efficiently and accurately detect APT attacks.

The present invention discloses a network traffic anomaly detection method based on online continuous learning, which uses an MLP neural network based on wavelet transform to detect network traffic anomalies in an online continuous learning manner, has continuous adaptive intelligent detection capabilities, efficient system performance, excellent detection effects, strong practicality, solves the model forgetting problem in traditional methods, balances real-time and accuracy, reduces resource consumption, improves system scalability, and sets a network traffic reconstruction model based on time series analysis theory and wavelet transform, which significantly improves computational efficiency while maintaining a high sensitivity to sudden anomalies. After using Haar wavelet to perform discrete wavelet transform on network traffic, only a single-layer MLP network is used to learn the network traffic pattern. This lightweight design greatly reduces resource occupation, enables the system to be flexibly deployed on various network devices, and effectively supports anomaly detection tasks in large-scale network environments.

The application relates to the field of artificial intelligence and power equipment operation and maintenance data processing technology, and particularly discloses a power energy storage operation and maintenance health evaluation method based on deep learning, which comprises the following steps: a pre-processing unit first performs caliber unification according to a historical caliber change set and extracts single data in real time, eliminates equipment differences, makes the algorithm identify only one set of caliber, and reduces special cases and error sources; an edge gateway performs clock consistency test, aligns the time axis, and avoids wrong time misjudgment; if the test fails, the data is improved and data reliability is output, and the caliber change set is rewritten and corrected; then, the data and the reliability are sent to a model to perform health evaluation and physical consistency verification, and the correlation is improved to be explainable physical reliability; finally, the evaluation result is mapped to a standardized operation and maintenance disposal suggestion, and the operation and maintenance plan is synchronized to a platform; the standardized action and plan are synchronized to realize a closed loop of diagnosis, disposal and recovery, and the whole process is auditable and reproducible.

The invention relates to a one-dimensional function fitting method and system based on input dimension expansion, and the method comprises the steps: firstly obtaining a target function and a training sample thereof, carrying out the dimension expansion of each input scalar according to an odd-dimension central symmetry rule: placing the scalar at the center of an expansion vector, and filling the two sides with the same constant related to a domain of definition; and constructing a neural network taking the extended vector as input, and adjusting the width of the hidden layer to enable the deviation between the total parameter number and the reference model not to exceed a set value, thereby realizing parameter redistribution. Then, a second-order optimization algorithm is used to train the network. During prediction, new input is input into the trained model after dimension expansion according to the same rule, and a predicted value is obtained. According to the method, parameter arrangement symmetry is actively broken, and the loss function landscape is remarkably improved.

The application provides a road surface state recognition method and device, and solves the problems of single road surface state recognition category, coarse result granularity and inability to distinguish specific road surface abnormal types caused by dependence on simple threshold judgment and shallow features in the prior art. The method comprises the following steps: acquiring a vertical vibration acceleration data segment of a vehicle in operation; extracting a characteristic value according to a preset optimal feature subset, wherein the subset comprises a frequency domain peak value and a spectrum energy; inputting the characteristic value into a classification model to obtain a recognition result, wherein the model is obtained by training based on data comprising at least three road surface state labels. Through fixed feature optimization, the application realizes fine recognition of multiple states such as well frame difference, expansion joint and deceleration strip, and significantly improves the calculation efficiency while ensuring the accuracy.

The present invention discloses a method and device for processing threat detection rules. The method includes: extracting the rule name of the threat detection rule, and pre-classifying the threat detection rule based on the extracted rule name of the threat detection rule to obtain a pre-classification result; each rule category corresponds to at least one threat detection rule; for each rule category in at least one rule category, determining the attack scenario corresponding to the rule category; applying at least one threat detection rule corresponding to the rule category to the attack scenario to obtain the threat detection result corresponding to each threat detection rule in the rule category; based on the threat detection result corresponding to each threat detection rule in the rule category, verifying and adjusting the classification of the threat detection rules in the rule category. The adoption of this solution can realize the accurate classification of threat detection rules, thereby facilitating the management of threat detection rules, thereby reducing storage overhead and system overhead.

This invention provides a multi-task learning resource optimization method based on the lottery hypothesis, belonging to the field of multi-task learning and neural network optimization technology. This method employs a sparse expert hybrid approach based on the lottery hypothesis to effectively prune neural network model parameters, thereby significantly reducing the computational cost and storage overhead of the neural network model. The sparse expert network structure retains high sensitivity to key task information. The introduction of a softmax-based router and mask matrix allows each task to select and activate suitable expert network components based on the features of its input data. Cross-training and iterative amplitude pruning optimize the omnipotent expert matrix, further enhancing the model's generalization ability in handling complex tasks and different data distributions. Task-specific layers independently process and optimize the features of each task, reducing mutual interference between tasks and improving the accuracy of feature extraction and the efficiency of task processing.