375 results about "Encoding algorithm" patented technology

The invention provides an industrial robot motion planning method based on a diffusion model. The method comprises the following steps: firstly, pre-training a point cloud encoder to compress and embed obstacle point cloud under task conditions into a potential space, and constructing a forward kinematics robot state coding algorithm and a path data collision detection module; then, task information is coded based on the pre-trained point cloud encoder, noise adding and noise reduction are carried out on task data based on a Transform diffusion model, and modeling is carried out on data probability distribution of a physical relationship between robot connecting rods; and finally, based on the trained neural network model, combining a small amount of motion feedback, generating an image from random Gaussian noise, and further improving the generation speed and the success rate. Finally, the neural network model can output an optimal path under a specific task condition.

The invention relates to the technical field of photography focusing control, in particular to a photography focusing control method based on machine vision. Comprising the following steps: collecting and preprocessing multi-frame image data; detecting a target area in the image through a machine vision technology and a deep learning algorithm, marking a main body object and dynamically adjusting a focusing priority; extracting multi-dimensional visual features by using a sparse coding algorithm, and constructing a dynamic focusing optimization model based on the features to calculate an optimal focusing parameter; the imaging definition and the focusing effect are evaluated in real time through a fuzzy logic control algorithm, and focusing parameters are adjusted according to feedback; and the photographic equipment is controlled to generate a high-resolution focused image or video data, so that the optimal imaging effect is ensured. According to the method, artificial intelligence and an optimization algorithm are utilized, the intelligent level of the photographic equipment is improved, the focusing capability and the imaging quality of the photographic equipment in a complex scene are improved, convenient and efficient photographing experience is provided for a user, and meanwhile dependence on manual operation is reduced.

The invention discloses an electromechanical equipment noise reduction method based on machine learning, and relates to the field of electromechanical equipment noise reduction, and the method comprises the steps: collecting sound pressure and vibration signals during the operation of electromechanical equipment, and collecting the operation state data of the electromechanical equipment; sparse reconstruction is carried out on the noise features; carrying out sound source localization on the reconstructed noise features by using a beam forming algorithm to obtain a noise distribution diagram; constructing a noise reduction model based on deep learning to obtain noise signals after noise reduction; extracting the frequency of the noise signal in a preset frequency range through a peak search algorithm; analyzing the acquired running state data by using a time-varying linear predictive coding algorithm TVLPC to obtain a noise frequency offset caused by the abnormal running state of the electromechanical equipment; generating an optimal solution by using a particle swarm algorithm; and according to the optimal solution of noise control, noise dominant frequency components are extracted for separation, and a plurality of phase-reversal noise reduction control signals are obtained. Aiming at low noise reduction precision of electromechanical equipment in the prior art, the noise reduction control precision is improved.

The invention discloses a financial document automatic auditing method and device and a medium, and relates to the technical field of financial reimbursement auditing. The method comprises the following steps: receiving a financial document to be audited and an associated attachment document, respectively extracting a first entity set, and extracting a second entity set from unstructured content; constructing a dynamic knowledge graph state space based on the entity set, wherein the dynamic knowledge graph state space comprises an entity vector generated by an entity embedding algorithm and a relation vector generated by a relation coding algorithm; defining a reinforcement learning action space, wherein the reinforcement learning action space comprises three types of atomic operations of newly adding and deleting a triple and adjusting confidence; in combination with the real-time document flow, the historical case library and the audit result data, dynamically evolving the knowledge graph through atomic operation, and calculating a value return value of each operation; pre-judging accumulated return values of different operation sequences by utilizing a Monte Carlo tree search algorithm, and pruning a low return sequence; executing the optimized operation sequence to update the knowledge graph; and finally, based on the updated atlas, triggering a logic verification rule to generate an auditing result.

The invention discloses an intelligent bid evaluation expert extraction and management system, and relates to the technical field of expert extraction systems. Comprising a data cleaning and compression module, a database logic and table relation design module, an expert information collection standard and automation module, an information real-time updating and principal management module, an extraction algorithm design module, a safety and authority management module and a real-time monitoring and auditing function module. Through a self-adaptive sparse auto-encoder and a dynamic clustering technology in the data cleaning and compression module, abnormal values in a bid evaluation expert database can be detected and cleaned, data compression is performed by adopting a self-adaptive dictionary coding algorithm, and the storage efficiency and query performance of the database are improved; based on a multi-modal information fusion and reinforcement learning method, accurate collection, dynamic updating and intelligent management of expert information are realized. The randomness and fairness of the bid evaluation expert extraction process are ensured by using a mode of combining deep reinforcement learning and a quantum random number generator in an extraction algorithm design module.

The invention relates to the technical field of electrical load management, and discloses an electrical load spatial-temporal distribution modeling and adaptive optimization regulation and control system. The system comprises a load monitoring module, an optimization decision-making module, an exception handling module, a communication coordination module and a data tracing module. The load monitoring module collects load data by using a space-time correlation analysis technology; the optimization decision-making module adopts a dynamic clustering algorithm to generate a regulation and control strategy; the exception handling module corrects load exception in a graded manner based on a multi-scale coordination mechanism; the communication coordination module realizes instruction interaction through a hierarchical routing protocol; and the data tracing module generates a topology storage index by using a multi-dimensional feature coding algorithm. The system can accurately collect load data, reasonably distribute power, effectively process load abnormity, guarantee communication stability, facilitate data tracing, improve the operation efficiency and reliability of an electric power system, and meet the requirement of a modern electric power system for electrical load management.

The invention relates to the technical field of display control, and discloses a method and device for controlling a display terminal through dynamic code matching, equipment and a storage medium. The method comprises the following steps: integrating various parameters of a code matching rule base, a code matching generator and a display terminal, and constructing a dynamic code matching system; obtaining input data of the display terminal based on the dynamic code matching system, and preprocessing and analyzing the input data to obtain a metadata structure containing data attributes and processing marks; carrying out code matching rule matching and parameter adjustment to obtain a configuration object containing a coding algorithm and key information; carrying out segmentation, transformation, encryption and mapping processing on the input data to obtain a coding data packet containing coding data and decoding information; performing fragmentation and secondary encryption processing to obtain a plurality of encrypted data fragments; according to the method and the device, the content of the display terminal can be safely and flexibly controlled by the aid of the method and the device, and the content of the display terminal can be safely and flexibly controlled by the aid of the method and the device.

The invention discloses a power grid subsynchronous oscillation suppression method based on a neural network prediction model, and particularly relates to the technical field of power grid control. The method comprises the following steps: extracting a high-dimensional feature vector of power grid measurement data and performing frequency domain decomposition to obtain feature distribution information of different frequency components; utilizing a sparse coding algorithm to realize mode separation, and identifying a running state abnormal region set; constructing a dynamic coupling model to evaluate the interaction relationship between the oscillation mode and the operation state in the abnormal region and the time evolution characteristic; analyzing modal distribution characteristics through adaptive filtering, and calculating a local modal separation degree; based on oscillation evolution information and a modal structure evaluation result, power grid operation parameters and suppression device configuration are dynamically adjusted, and the recognition and response capability of a neural network to a potential dangerous subsynchronous oscillation mode is effectively improved.

The invention relates to the technical field of product test data management, and discloses a product test data storage and management system based on an MES system. The system comprises a dynamic configuration module which can use a self-adaptive parameter adjustment algorithm to configure data acquisition parameters according to experiment types; the closed-loop feedback module is used for binding experimental data with production batches and process parameters based on a real-time association algorithm; the two-stage verification module is used for detecting data exception through a double-layer cascade verification algorithm; and the intelligent storage module is used for generating hierarchical storage labels by using a multi-dimensional structured coding algorithm. In addition, the system integration module realizes data interaction with ERP and SCADA systems based on an interface adaptation algorithm. The system solves the problems that in a traditional data management mode, collection parameters are fixed, data relevance is poor, a verification mechanism is imperfect, storage and retrieval are inconvenient and the like, data can be efficiently collected, accurately associated, strictly verified and intelligently stored, and the enterprise data management level and production competitiveness are improved.

The invention discloses a sprint learning path optimization method based on an improved whale optimization algorithm. The problem that existing sprint path planning is unreliable is solved. The method comprises the following steps: constructing a total learning cost objective function; using a random key coding algorithm based on chaotic mapping to represent the learning paths in a sequence mode, and using serial scheduling to obtain a plurality of candidate sprint learning paths; introducing an adaptive weight mechanism, an examination driving offset item and a Gaussian disturbance item, and adopting an improved white whale optimization algorithm to carry out iterative optimization on the candidate sprint learning paths to obtain a plurality of iteratively optimized sprint learning paths; in any iterative optimization, if the current candidate sprint learning path violates a first repair relation or / and a time window or / and a daily learning duration budget in the learning path, performing feasibility repair on the current candidate sprint learning path; performing neighborhood local search on the iteratively optimized sprint learning path based on a forgetting risk index; and outputting an optimal sprint learning path.

The invention provides an inadvertent dynamic searchable encryption method, and relates to the technical field of information security, and the method comprises the steps: constructing a new Labeled PSI technology in combination with a VOLE technology based on a receiver processor and a sender processor in a bottom layer module, and obtaining an intersection Z of the two parties and a corresponding label; and in the application layer, the client and the server negotiate a database updating method together to dynamically update the database, and the client and the server negotiate a database searching method together to decrypt the document so as to complete encryption processing which can be searched for dynamically and accidentally. According to the invention, the security and efficiency of the dynamic searchable encryption technology are improved.

The invention discloses a data verification method, device and equipment and a medium, and belongs to the technical field of digital verification, and the method comprises the steps: receiving to-be-verified data information; creating a target basis matrix, and performing short ring and trap set elimination optimization on the target basis matrix; encoding the data information to be verified through a fast encoding algorithm; and performing data verification on the encoded to-be-verified data information through the optimized target basis matrix. According to the basis matrix provided by the invention, the storage space of the check matrix can be reduced, the performance of the check matrix can be improved, and meanwhile, the digital channel can be quickly checked.

The invention discloses a printed circuit board withstand voltage detection and evaluation method, which comprises the following steps: in the whole withstand voltage detection process, aiming at different process batches and environments, acquiring electrical signals, process parameters, environment variables and multi-modal visual data, and carrying out time sequence synchronization marking and standardized preprocessing; extracting multi-source features by using a feature coding algorithm, and establishing a multi-mode distribution model covering signal mutation, visual defects and working condition anomalies; through cross-modal feature fusion and a time sequence causal inference model, an abnormal event causal relationship graph is automatically constructed, and a voltage withstanding failure root cause chain is identified and positioned; according to the method, the efficient structured management of the detection data, the accurate traceability of the abnormal reasons and the cross-batch analysis are realized, and the capability of diagnosing and preventing the withstand voltage abnormality of the printed circuit board is remarkably improved.

The invention discloses a method for generating a three-dimensional building GLTF file based on index compression. The method comprises the following steps: S1, collecting three-dimensional coordinate data of a building; s2, grouping and classifying the data according to floors to form a classification index structure based on a hash table; s3, selecting a reference model and recording Mesh data and vertex coordinates of the reference model; s4, calculating a translation matrix T of the polyhedral structure, and performing coordinate transformation; s5, generating Mesh data of other polyhedrons based on index compression, and reducing redundant surface data by adopting a topology preserving surface optimization algorithm; s6, independently processing the geometric data of each floor, and generating corresponding Mesh data and Node nodes; and S7, combining all floor data to generate a complete GLTF file, and performing compression by using a dynamic Huffman coding algorithm. Through the improved index compression and dynamic Huffman coding technology, the file size is remarkably reduced, the transmission efficiency is improved, and the geometric and topological integrity of the model is ensured.

The invention relates to the technical field of image transmission, in particular to a remote ultrasonic image transmission system. The remote ultrasonic image transmission system comprises an ultrasonic image feature extraction module, a bandwidth dynamic adaptation module, a real-time annotation information coding module, an HDR image processing module, a network condition monitoring module, an image and annotation synchronous transmission module and a client decoding display module. According to the method, the ultrasonic image is analyzed through a deep learning algorithm, the network bandwidth is dynamically adapted, annotation information transmission is optimized through a differential coding algorithm, the visual effect of the image is improved through local contrast enhancement and HDR processing, synchronous transmission of the image and the annotation is guaranteed, and the quality and efficiency of remote medical image transmission are improved; accurate display of important details is guaranteed through accurate processing of diagnosis-related key areas, diagnosis accuracy is improved, network fluctuation is effectively coped with through a dynamic adjustment strategy of network bandwidth, transmission stability is guaranteed, and accessibility and quality of medical services are remarkably improved.

The invention relates to the technical field of photovoltaic power generation modeling and simulation, in particular to a photovoltaic model parameter identification method and system based on an improved symbiont search algorithm. A complex number coding mechanism is introduced on the basis of a traditional SOS algorithm and is expanded from original one-dimensional real number coding to a two-dimensional complex number coding space, so that the search range of a group is expanded, and the optimization capacity and speed of the algorithm are enhanced; establishing a photovoltaic model, and determining a to-be-identified parameter solution vector; adopting a root-mean-square error as a parameter identification objective function of the photovoltaic model; and an improved complex number coding SOS algorithm is operated to identify unknown parameters of the photovoltaic model so as to minimize the difference between actually measured current data and a simulation result, thereby pursuing an optimal fitting effect between the actually measured current data and the simulation result. The improved algorithm provided by the invention has good applicability in the parameter identification process of a single-diode model, a double-diode model and a photovoltaic module model and can quickly optimize, prediction errors are effectively reduced in the aspect of balancing global and local optimal solutions, and the parameter identification precision is improved.

The invention discloses a data processing method and a related device, and the method comprises the steps: firstly obtaining a target instruction of a virtual machine, and enabling the target instruction to be used for controlling the virtual machine; and then according to the target virtual memory address corresponding to the target instruction, an encryption key corresponding to the target instruction is determined, and different virtual memory addresses correspond to different encryption keys. And according to the encryption key, performing instruction coding on the target instruction through a coding algorithm to obtain a coding result, and storing the coding result as the target instruction in the target virtual memory address. Finally, in the running process of the virtual machine, when a calling request for the target instruction is obtained, the coding result is read from the target virtual memory address, instruction decoding is conducted on the coding result through a decoding algorithm corresponding to the coding algorithm according to the encryption key, and the target instruction is obtained. The encryption key is generated based on the storage address of the instruction, and the encryption key is used for instruction coding to realize the specificity of a coding result, so that the security of the virtual machine is improved.

The invention relates to the technical field of data information retrieval, and discloses an efficient batch privacy information retrieval method and system based on OCD and GPU optimizing.The efficient batch privacy information retrieval method comprises the steps that firstly, a server is initialized, a database is subjected to bucket division through a PBC encoding algorithm based on the client query number, the database subjected to bucket division is obtained, and when a user conducts information retrieval, the user can retrieve privacy information in a batch mode; the method comprises the following steps of: finding a sub-bucket position of a retrieval request in a server at a client by using a PBC scheduling algorithm, carrying out homomorphic encryption on the retrieval request in each sub-bucket, compressing all the retrieval requests by using an OCD to obtain a retrieval ciphertext, sending the retrieval ciphertext to the server, and sending the retrieval ciphertext to the server according to the retrieval ciphertext provided by the client. The retrieval result in the ciphertext form is obtained and returned to the client side, the client side decrypts the retrieval result in the ciphertext form, and the required retrieval result in the plaintext form is obtained. According to the method, the GPU is used for parallel processing, computing resources in a real scene can be fully utilized, and the computing efficiency of privacy information retrieval is remarkably improved.

The invention relates to the technical field of Internet of Things data management. The electrical equipment management system based on the Internet of Things comprises a hierarchical edge compression module used for performing hierarchical edge compression processing on unstructured data to generate a lightweight data packet; the hierarchical transmission scheduling module is used for performing priority marking and network slice mapping processing on the lightweight data packets to obtain hierarchical transmission data streams; the edge cloud collaboration module is used for carrying out edge-cloud collaboration processing based on the hierarchical transmission data stream to generate structured metadata and a depth analysis result; and the state linkage control module is used for carrying out state linkage management on the electrical equipment according to the structured metadata and the deep analysis result and generating an equipment control instruction so as to solve the problems that the compression efficiency and fidelity of a fixed coding algorithm are unbalanced, network channel priority distribution is rigid and equipment control and data flow dynamic linkage is missing.

The invention discloses a copper product quality defect traceability system and method. The system comprises a data acquisition module, a data processing module, a quality detection module and a defect traceability module. Relates to the technical field of copper product quality defect traceability, and solves the technical problem that quality defect information of a copper product is difficult to accurately obtain in the prior art. The method comprises the following steps: calculating a quality evaluation coefficient based on surface defect characteristics and performance data; integrating the quality evaluation coefficient and the basic information into traceability information; coding the traceability information based on a coding algorithm and storing the coded traceability information in a database; and performing defect traceability on the copper product based on the quality evaluation result and the traceability code. The performance standard-reaching rate is calculated according to the performance data and the preset performance standard, and the performance standard-reaching rate corresponding to each piece of performance data is multiplied by the corresponding proportionality coefficient to obtain the second evaluation coefficient, so that the performance of the copper product can be accurately reflected, and the quality defect tracing accuracy of the copper product can be improved.

The invention discloses a cross-platform file incremental synchronization method, a computer device, a medium and a product. The method comprises the following steps: monitoring a determined to-be-monitored file in real time, obtaining file change information, dynamically obtaining a target coding algorithm and a target compression ratio, processing the file change information, obtaining intermediate file information, distributing a unique identifier and unique verification information for the intermediate file information, and obtaining target file data; detecting and acquiring a plurality of target network paths; and according to the network delay information, the bandwidth information and the reliability information, determining an optimal transmission path in the plurality of target network paths, and then transmitting the target file data on the path. The method is suitable for different operating systems and platforms, the optimal network path can be detected and selected for file transmission, and it is ensured that cross-platform file synchronization can be efficiently, stably and safely carried out under different network conditions.

The invention relates to an encrypted network traffic classification method based on a pre-trained large language model, and belongs to the technical field of encrypted network traffic classification. The method mainly comprises three stages: a pre-training stage: converting original encrypted network traffic data into a double-byte hexadecimal format through preprocessing, generating and optimizing a basic vocabulary by using a byte pair coding algorithm, then constructing a large language model, and obtaining a pre-training model through distributed training; in the retraining stage, the data is subjected to head byte shuffling processing, and the pre-training model is quickly retrained to improve the generalization ability. In the fine tuning stage, to-be-classified data is preprocessed to generate hexadecimal double-byte data with labels, and classification task training is performed by using the retrained model to obtain an encrypted network traffic classification fine tuning model and classification accuracy. Through the combination of pre-training and retraining and the fine tuning of the pre-training model by means of data classification, the efficient processing and accurate classification of the complex encrypted network traffic are realized.

The invention relates to the technical field of wireless connection of conference machine networking. The method comprises the following steps: converting an AP hotspot name and an AP hotspot password into a character string in a preset format, converting the character string into a coded audio signal based on a sound wave coding algorithm, playing the coded audio signal by using host equipment, and sending the coded audio signal to the host equipment; the method comprises the following steps: acquiring a coded audio signal recorded by slave equipment, performing spectral analysis on the coded audio signal by using a fast Fourier transform algorithm to obtain a spectral analysis result, extracting a corresponding sine wave frequency from the spectral analysis result, decoding the sine wave frequency into an AP hot spot name and an AP hot spot password based on a preset frequency character mapping relationship, and sending the AP hot spot name and the AP hot spot password to the slave equipment. And the slave device is connected to the wireless AP created by the host device based on the AP hot spot name and the AP hot spot password, and the slave device sends networking confirmation information to the host device to complete wireless networking. The present invention has an effect of improving wireless cooperation efficiency between devices.

The invention provides a low-power-consumption embedded data recording system and algorithm for on-line monitoring of SF density. The system is provided with an MCU main control unit, an external independent watchdog circuit, a LoRa wireless communication module, an RS485 interface, an SF density and temperature measurement sensor, a battery power supply and voltage acquisition module and a Flash data storage and real-time clock module. Algorithms of the system comprise an intelligent data recording strategy, a dynamic sensor activation and sleep mechanism, a power failure data protection and breakpoint continuation mechanism, a multi-parameter compression coding algorithm, a dynamic sampling frequency adjustment strategy, a real-time clock synchronization and timestamp calibration mechanism, a multi-sensor fusion sensing mechanism and an ultra-low power consumption system architecture mechanism. The low-power-consumption embedded data recording system and algorithm for on-line monitoring of the SF density have the functions of multi-data change trend intelligent judgment, multi-data fusion perception judgment, ultra-low power consumption and flexible communication, and are suitable for long-term field deployment.

The invention relates to the technical field of data processing, in particular to a remote data migration backup method and system, and the method comprises the steps: obtaining the temperature and current during the operation of a machine, and constructing a cooperative relation curve; and segmenting the collaborative relation curve by using the extreme point of the collaborative relation curve, and calculating the difference between the normality of any data in any segment and the average normality of adjacent data to obtain the anomaly index of the data. The interference degree of the data is calculated, the temperature or the current at the corresponding moment is corrected based on the interference degree, the correction direction is the direction close to the adjacent data, the correction amplitude is in positive correlation with the interference degree, and correction values of the temperature and the current are compressed through a Huffman coding algorithm so that migration backup can be conducted. According to the method, the data compression efficiency and the accuracy of the compression result in the data migration backup process can be improved.

The invention provides an end-to-end voice transmission method based on a low-orbit satellite communication vocoder channel, which has efficient data compression: a G.729 coding algorithm and a linear predictive coding technology are adopted, so that the bit rate of voice data is reduced to 8kbps; encryption processing is introduced after voice coding, and confidentiality and integrity of data in the transmission process are ensured. The anti-interference capability is excellent: by using the QPSK modulation technology, the anti-interference performance of the signal in long-distance transmission is improved, the QPSK modulation can transmit 2-bit information in each symbol, and the frequency spectrum utilization rate is higher; in the real-time feedback mechanism, under the support of the channel state feedback mechanism, the receiving end can report channel quality information to the sending end regularly, and the modulation mode and power setting are adjusted in real time according to feedback so as to optimize the transmission quality; and comprehensive signal processing: through soft decision decoding and a combined signal processing technology, the signal recovery capability is obviously enhanced, so that the decoding process is more accurate.

The invention provides a bearing fault diagnosis method and system, and relates to the technical field of bearing fault detection, and the method comprises the steps: collecting bearing vibration data; the bearing vibration signals are converted into a wavelet two-dimensional time-frequency image, an angle field two-dimensional time-frequency image and a transfer field two-dimensional time-frequency image through a continuous wavelet transform algorithm, a Grubrum angle field coding algorithm and a Markov transfer field algorithm; fusing the wavelet two-dimensional time-frequency image, the angular field two-dimensional time-frequency image and the transfer field two-dimensional time-frequency image to generate a fused time-frequency image; constructing a bearing fault detection model with a Swinin-T channel, a mixed attention channel and a feature fusion module; inputting the bearing vibration data as a training set into a bearing fault detection model until the predicted loss value of the bearing fault detection model is within a preset predicted loss value interval; acquiring a vibration signal of the to-be-detected bearing; and inputting a vibration signal of the bearing to be detected into the trained bearing fault detection model, and outputting a predicted bearing fault type.

The invention discloses a three-dimensional light field content construction and editing method, which relates to the field of naked eye three-dimensional display, and comprises the following steps of: collecting multi-view images of a real scene through a camera, and constructing a unified three-dimensional space coordinate system; generating an initial three-dimensional scene model; performing adjustable content editing on each frame of multi-view image; updating the initial three-dimensional scene model through the edited image; the updated three-dimensional scene model is rendered through a shear type rendering method, and an image sequence with multi-view consistency is generated; and coding the generated image sequence into three-dimensional light field content by using a light field coding algorithm. According to the method, the real static scene can be efficiently converted into the three-dimensional light field content with a complete structure and a continuous visual angle through multi-visual-angle image acquisition, three-dimensional reconstruction and rendering processing, so that the method has stronger sense of space depth and reality, and the visual experience and the display effect of the three-dimensional light field content are remarkably improved.

The invention relates to a multiplier based on radix-4Booth coding and an improved Wallace compression tree, and the multiplier comprises a partial product generation module which recodes an input multiplier into a plurality of partial products through employing a Radix-4Booth algorithm, and forms a compact partial product array; the partial product compression module is used for quickly compressing the partial product through two compressors by utilizing an improved Wallace tree compression structure, and reducing the calculation path delay; and the final summation module is used for summing the compressed partial product by using a high-speed summator to obtain a final multiplication result. According to the invention, a radix-4Booth coding algorithm is adopted, so that a partial product array is regularly arranged, the circuit area is reduced, a new tree type compression structure in which a 3-2 compressor and a 4-2 compressor are mixed is adopted to realize a Wallace tree, the compression efficiency is improved, and the key delay is reduced.

A processor-implemented method including instructing a receiver to generate an OKVS (oblivious key-value store) matrix by applying an OKVS encoding algorithm to first data and hash data of the first data, generating a vector or scalar parameters satisfying a preset linear equation using vector oblivious linear evaluation (VOLE) for a subspace based on Minicrypt, and distributing a preset first group and a preset second group among the parameters to a sender and a receiver, respectively, instructing receiver to generate a masking matrix by masking the OKVS matrix to a first vector included in the first group and transmit the masking matrix to the sender, instructing the sender to update one of the vectors included in the second group using the masking matrix, and instructing the receiver to generate first comparison data by applying an OKVS decoding algorithm to a second vector included in the first group and the first data.