63 results about "Three dimensional matrix" patented technology

The invention discloses a medicine supply chain data management system and method based on data analysis, and relates to the technical field of data analysis. The system comprises a demand urgency evaluation module, an inventory authenticity verification module, a transportation path reliability evaluation module and an optimal allocation scheme screening module. According to the method, the demand urgency score is calculated by collecting the medical resource and material consumption data of the demand point; screening adaptive warehouses and verifying the authenticity of the inventory; evaluating the reliability of the transportation path; and a three-dimensional matrix is constructed, a comprehensive score is calculated through dynamic weighting, and an optimal'demand point-warehouse-path 'combination is screened. According to the invention, quantitative decision and global cooperation of emergency material allocation are realized, the response efficiency and accuracy are improved, and the method is suitable for material guarantee in public health emergencies.

The invention relates to the technical field of power communication network fault diagnosis, in particular to a power communication network fault intelligent diagnosis method based on dynamic topology simulation. The electric power communication network fault intelligent diagnosis scheme is implemented by six steps: firstly, collecting physical layer, protocol layer and topological data, and constructing an equipment-time-performance index three-dimensional matrix; decomposing the matrix to calculate a stability index, and primarily judging an abnormal time period and an abnormal area; mapping a physical topology, and accurately positioning a fault area by using a dynamic convex hull and a ray algorithm; extracting historical data to generate a characteristic curve, and matching a sample library to identify a fault type; the theoretical and actual parameters are iteratively compared to verify the reliability; and finally generating a report and matching a repair strategy. Rapid positioning, accurate identification and reliable diagnosis of faults are achieved, the defects of static analysis, data splitting and verification deficiency in the prior art are overcome, and the operation and maintenance efficiency and the safety level of an electric power communication network are improved.

The invention discloses a power distribution network self-healing strategy generation method and system which are used for solving the technical problem that when an existing power distribution network self-healing strategy generation method faces a fault, simple label matching is carried out only according to a fault type and a line basic structure, and the rapid power restoration quality of a power distribution network is poor. The method comprises the following steps: acquiring surface temperature distribution data of power distribution network equipment and abrupt change high-frequency transient signals of power distribution line nodes; constructing a dynamic topological graph of the power distribution network, and constructing a dynamic three-dimensional matrix according to the dynamic topological graph of the power distribution network, the surface temperature distribution data of the power distribution network equipment and the abrupt change high-frequency transient signals of the power distribution line nodes; generating a plurality of priority self-healing strategies according to a preset basic strategy library, the power distribution network optimization function and the power distribution network constraint conditions; and based on a fuzzy analytic hierarchy process, screening each priority self-healing strategy by adopting a power distribution network optimization function according to the dynamic three-dimensional matrix, and determining a target power distribution network self-healing strategy.

The invention discloses a molecular dynamics modeling method and system for a porous medium, and relates to the technical field of molecular dynamics modeling, and the method comprises the following steps: recognizing pore voxel regions and non-pore voxel regions based on a three-dimensional matrix, and carrying out the boundary division of a plurality of non-pore voxel regions, and obtaining a boundary layer anchor point pool and a non-boundary layer anchor point pool; dividing the boundary layer anchor point pool and the non-boundary layer anchor point pool into a plurality of grid units, and filling the anchor points in the grid units through the to-be-filled molecules based on the target filling number and the target mask value; randomly rotating the current to-be-filled molecule, and if the distance between the current to-be-filled molecule and the placed atom is smaller than the corresponding minimum distance threshold value, abandoning the current placement; and after filling of all molecules is finished, counting space absolute coordinates of all atoms, and outputting a coordinate file. According to the method, accurate and non-overlapping placement of molecules in a complex pore structure is realized, and establishment of a porous medium complex random pore microcosmic molecular dynamics model can be realized.

The application relates to the fields of information security and image processing, and discloses a multi-image parallel encryption processing method based on an improved chaotic mapping, which comprises the following steps: separating and stacking each color channel of multiple images to be encrypted to construct a three-dimensional pixel matrix; extracting a matrix hash digest to be converted into a sub-key; inputting the sub-key into an improved chaotic mapping model of a historical state variable to iteratively generate a scrambling and diffusion sequence; performing sorting scrambling on the three-dimensional matrix in multiple orthogonal dimension sections by using the sequence; mapping the sequence into a shift step length to perform a cyclic shift and a bitwise XOR linkage diffusion on the pixels; and finally, outputting ciphertext images by segmentation. The application breaks the physical isolation of a single image to realize spatial depth mixing by constructing a three-dimensional matrix and performing cross-dimension scrambling; the chaotic dynamics degradation under limited precision is overcome by means of a historical state delay; and the efficiency of multi-image encryption and the system attack resistance are improved by using a shift and XOR linkage to realize bit-level depth confusion.

The application provides a cylindrical array sonar system and a target detection method based on high-resolution beam forming. The method comprises the following steps: converting a beam forming weight vector of a received sound wave signal into a Kronecker product, combining a joint probability density model to construct a negative log-likelihood function, and optimizing a vector in an azimuth dimension and an elevation dimension through a preset direction constraint condition to form a high-resolution beam; performing correlation analysis on echo signals reflected by a target at different azimuth angles and elevation angles and on transmitted sound wave signals at different time delays; constructing a three-dimensional matrix containing a distance, an azimuth angle and an elevation angle according to a correlation analysis result, performing normalization processing on the three-dimensional matrix to eliminate signal intensity differences, and outputting a target detection positioning result. Through the combination of Kronecker integral decomposition and three-dimensional matrix modeling, the application realizes high-resolution joint positioning detection of a target in an azimuth angle, an elevation angle and a distance dimension, effectively eliminates signal intensity differences, and improves positioning accuracy.

The invention discloses a power transmission line traveling wave fault positioning method and system based on data analysis, and relates to the technical field of big data analysis, and the method comprises the steps: firstly collecting fault original records, temperature, humidity and weather states, and building a historical library; carrying out time-frequency domain conjoint analysis on the traveling wave signal, and calculating a phase difference and a time difference; the signals are adaptively decomposed to different frequency bands to calculate fractal dimensions, and advanced features are extracted in combination with a deep convolutional neural network; associating the phase difference, the instantaneous electric field intensity and the magnetic field rotation direction to construct a three-dimensional matrix rotation graph structure, and forming a high-dimensional feature vector by using a graph attention network; and calculating a fault posterior probability according to the high-dimensional vector, dynamically adjusting a threshold value according to an environment parameter, and generating an adversarial network virtual sample to match a fault type when the probability is insufficient. The system comprises a data acquisition module, a time-frequency analysis module, a feature extraction module, a feature association module and a fault determination module, all the modules cooperate to realize data acquisition, feature analysis and fault positioning, and the positioning reliability is enhanced.

The invention discloses a multi-channel injection system and method for an ultra-large rock core hydraulic fracturing experiment. The system comprises a three-dimensional injection array module, a modular injection panel, a variable impedance regulation and control unit, a dual-medium consistency injection module and a fluid-solid coupling compensation control unit. The method comprises the following steps: constructing a three-dimensional matrix type injection network, preparing sand-carrying fluid with uniform concentration, configuring initial fluid impedance to realize multi-channel flow matching, starting synchronous injection, monitoring pressure and stress signals in real time, and dynamically adjusting the impedance of each channel to compensate the heterogeneity of the rock core. And finally, a uniform and stable volumetric pressure field is formed and maintained in the ultra-large rock core, and meanwhile, the concentration deviation of the sand-carrying fluid between the channels is controlled within a preset threshold value. The technical problems that a pressure field in a large-scale rock core is not uniform, proppant conveying is not stable and multi-channel interference is not stable are solved, and the method is suitable for complex crack expansion and proppant laying mechanism research.

The invention discloses a two-dimensional wave field reconstruction method based on compressed sampling, and belongs to the technical field of signal processing and sensors. According to the method, original time domain signals of a two-dimensional wave field are expressed as a three-dimensional matrix, random sampling is carried out through a Bernoulli distribution random sampling matrix, after the three-dimensional matrix is reduced into a two-dimensional matrix, a group sparse perception convolutional neural network is constructed to serve as a non-convex optimization solver, a real part and an imaginary part of a complex signal are processed, group sparse constraint is applied, and a non-convex optimization solver is constructed. And reconstructing the basis coefficient matrix to obtain an original signal reconstruction result, and finally raising the dimension to restore the original signal into a three-dimensional wave field. According to the method, the sampling rate is reduced, the reconstruction precision is improved, large-scale data set training is not needed, each training is an independent reconstruction process, and the method is suitable for the fields of acoustic imaging, seismic exploration and the like.

The invention relates to a method (100) for compressing an image stack (PIL) comprising N images, each image being represented by a two-dimensional data matrix (U, V), each cell (u, v) of the matrix comprising a set of values representing a pixel of the image, which method (100) comprises the following steps: - stacking (110) the images in a three-dimensional matrix (U, V, W) comprising N planes W1-Wn in the dimension W, each plane Wi in the dimension W corresponding to an image, and each cell (u, v) in the plane Wi comprising a set of values representing a pixel of the image; and - compressing (120) the stack of images from the three-dimensional matrix, using a dictionary compression algorithm. The invention also relates to a computer program, a device, a system and a vehicle implementing such a method.

The invention belongs to the technical field of material science foundation, and discloses a single twinborn system dominated twinborn transmission probability prediction method. According to the method, aiming at the condition that a single twin system is started preferentially under specific texture and force field conditions, the uniqueness of the twin system is limited and activated, and a three-dimensional matrix expression system of specific twin system spatial orientation is constructed. And based on a judgment rule that twin crystal faces and twin directions of the two crystal grains are parallel, calculating the minimum orientation difference capable of realizing parallel twin systems among the crystal grains. According to the method, the twinborn orientation difference is provided as a key physical quantity for quantifying the twinborn transmission capability, and the regulation and control mechanism of single twinborn system activation on local stress redistribution is disclosed by eliminating the interference of a non-activated twinborn system. The method is applicable to a twin-dominated deformation system at low temperature or high strain rate, and provides a micromechanical basis for optimizing strain coordination at the grain boundary.

The application discloses an Alzheimer's disease prediction method and system based on a millimeter wave radar. The method comprises the following steps: reorganizing original data of human gait collected by the millimeter wave radar to obtain a three-dimensional matrix block, and operating the three-dimensional matrix block to obtain multi-modal data; performing feature extraction, alignment and fusion on the multi-modal data and text data to obtain reweighted multi-modal data; training a GLM by using the multi-modal data and the text data to obtain a trained GLM, and inputting the reweighted multi-modal data into the model to obtain signal semantic features; inputting the signal semantic features and random noise into a diffusion model and training the diffusion model to obtain a trained diffusion model; and generating an image result by using the trained diffusion model, and combining a text prediction result to assist a user in predicting Alzheimer's disease. The application can combine the advantages of millimeter waves and multi-modal large models, improve the robustness and generalization ability of the model, and improve the early prediction ability of Alzheimer's disease.

The invention relates to the technical field of data management, in particular to a digitization-based operation data management method and system, which comprises a business processing layer and a business application layer, acquires multi-source data, performs preprocessing and format conversion on the acquired multi-source data to obtain standardized data, and simultaneously, performs operation data management on the basis of a set trigger condition. Performing relation mining on the standardized data based on the constructed knowledge graph model, and performing dynamic early warning at the same time; performing double-board collaborative analysis based on the standardized data, and performing task intelligent scheduling; early warning and visual display are carried out on a three-dimensional matrix of time, space and responsibility until a task is completed, unified analysis can be carried out on multi-source data, the multi-dimensional dynamic analysis capability is achieved, and the intelligence of decision making is ensured.

The invention discloses a water conservancy risk prediction method based on evolution characteristics, and belongs to the technical field of water conservancy projects. The method comprises the following steps: dividing a to-be-detected area into a plurality of grids, collecting and normalizing surface and internal water conservancy risk factors of each grid, constructing a surface and internal evolution incidence matrix, extracting collaborative strength and total evolution strength, and splicing to form an evolution characteristic matrix; determining an evolution characteristic transfer coefficient based on the distance between the to-be-predicted grid and the neighborhood grid, and obtaining a neighborhood evolution characteristic transfer matrix; and inputting the multi-moment evolution characteristic three-dimensional matrix and the neighborhood evolution characteristic transmission three-dimensional matrix into a water conservancy risk prediction network, and outputting a grid water conservancy risk score. According to the method, high-precision prediction of the water conservancy risk is realized by capturing a risk factor space-time evolution rule and a neighborhood transfer effect.

The invention discloses a knowledge base intelligent retrieval and knowledge reasoning method and system oriented to city styles and features, and the method comprises the steps: collecting multi-source city data of a preset city, and carrying out the preprocessing of the multi-source city data; constructing a three-dimensional matrix based on style stability, style freshness and development kinetic energy through the multi-source city data, dividing city modes according to the three-dimensional matrix to obtain mode data, and constructing a style topology network according to the multi-source city data and the mode data; a graph neural network is adopted to carry out community detection on the text topology network to identify a style and appearance cluster, the style and appearance cluster is evaluated according to a four-quadrant matrix to obtain a text value degree, and style and appearance causes are deduced according to the text topology network; and constructing an intelligent retrieval and knowledge reasoning model of the urban style and appearance knowledge base according to the value degree of the veins, optimizing the intelligent retrieval and knowledge reasoning model of the urban style and appearance knowledge base according to the causes of the style and appearance, and outputting a retrieval reasoning result.

The invention provides a three-dimensional joint parameter estimation method based on Wi-Fi (Wireless Fidelity) beam forming feedback information (BFI). The three-dimensional joint parameter estimation method based on the Wi-Fi beam forming feedback information (BFI) is characterized in that the three-dimensional joint parameter estimation method based on the Wi-Fi beam forming feedback information (BFI) is provided; the method comprises the following steps: firstly, acquiring BFI of a direct path and a target reflection path between transceivers; secondly, constructing the BFI into a three-dimensional matrix about three dimensions of an antenna, a subcarrier and a data packet, performing smooth decoherence processing on the matrix, and calculating a covariance matrix of the smoothed matrix; secondly, performing characteristic decomposition on the covariance matrix to obtain a signal subspace and a noise subspace; and finally, constructing a spectral function, and obtaining an angle of departure (AOD) between a direct path and a target reflection path, relative time delay and relative Doppler frequency shift through spectral peak search. Based on the three parameters obtained through estimation, the method can be applied to scenes such as positioning or breathing monitoring. According to the method, complete channel state information (CSI) does not need to be accessed, the CSI can be directly extracted from the communication process, and the method has natural robustness for carrier frequency offset and sampling frequency offset, and is easier to realize large-scale popularization and application.

The invention discloses a dynamic control method and system for a deformable public building space, and the method comprises the following steps: synchronously collecting the timestamp, people flow density, crowd movement characteristics and environment parameters of the deformable public building space through a space-time-crowd integrated sensor network deployed in a whole domain of a building; s2, a multi-source data unified analysis algorithm is adopted, integrated processing is conducted on the mixed data collected in the S1, and a space demand portrait with a time label, a space label and a crowd attribute label is output. Time periods, crowd attributes and space resource parameters serve as equal weight input items through a three-dimensional matrix operation decision model; fusion matching of requirements and deformation is directly achieved, an integrated instruction is output, the delay problem of traditional step-by-step decision making is avoided, decision making time consumption is greatly shortened, it is ensured that building space deformation response is faster, and dynamic requirements of various time periods are met.

The invention relates to the technical field of image information security, and discloses an image encryption method and system based on hyperchaotic driving and a reversible cellular automaton. The method comprises the following steps: generating a driving parameter and an initial value of the time delay three-dimensional memristor hyperchaotic system according to a plaintext image and an external key; using the driving parameters and the initial values to obtain multiple groups of chaotic sequences; performing pixel-level scrambling on a plaintext to be encrypted to obtain a three-dimensional matrix; performing bit-level scrambling on the three-dimensional matrix by using a chaos sequence to obtain a scrambled three-dimensional matrix; based on the chaos sequence, constructing a dynamic rule pool and an intermediate configuration matrix for reversible second-order cellular automaton evolution; taking the scrambled three-dimensional matrix as an initial cellular state of the reversible second-order cellular automaton, and executing second-order evolution of the reversible second-order cellular automaton in combination with the intermediate configuration matrix and the dynamic rule pool to generate a ciphertext matrix; and reconstructing the ciphertext matrix into a ciphertext image. According to the invention, the randomness, attack resistance and reliability of image encryption can be improved.

The application provides a behavior state determination method and device, equipment and a medium, which are used to solve the problem of possible errors when measuring chest displacement, breathing displacement and heartbeat displacement based on the prior art. In the embodiment of the application, a first set number of chest displacements, breathing displacements and heartbeat displacements within a set time period are collected based on a millimeter wave radar, each second chest displacement vector, each second breathing displacement vector and each second heartbeat displacement vector are generated, each second chest displacement vector, each second breathing displacement vector and each second heartbeat displacement vector are superimposed, and a three-dimensional matrix is generated. The three-dimensional matrix is input into a neural network model to obtain a state value, thereby determining whether a user is in a detection state. This is because the measurement of breathing and heartbeat during movement also has an impact on the detected breathing and heartbeat, and therefore the current state of the user can be accurately determined based on the measured breathing and heartbeat, which can improve the accuracy of the measurement.

The present application relates to the technical field of image reconstruction, in particular to a liver tumor interventional preoperative blood vessel three-dimensional model reconstruction system, which can realize the following steps through the mutual cooperation between multiple modules: obtaining a liver CTA three-dimensional matrix of a target patient before liver tumor intervention; determining the blood vessel confidence corresponding to each voxel in the tumor peripheral region and the normal liver region; determining the gradient distortion degree corresponding to each voxel in the tumor peripheral region; determining the effective blood vessel proportion in the tumor peripheral region; performing adaptive blood vessel fracture completion screening processing according to the gradient distortion degree and the blood vessel confidence to obtain the tumor peripheral blood vessel region; and performing blood vessel reconstruction based on the effective blood vessel proportion in the tumor peripheral region and the gray value corresponding to the voxel in the tumor peripheral blood vessel region. The present application realizes the identification of the tumor peripheral blood vessel region and improves the accuracy of identification, thereby improving the rationality of liver tumor interventional preoperative blood vessel three-dimensional model reconstruction.

The invention provides a future price prediction method based on time sequence attention and a dynamic graph of future varieties, and the method comprises the following steps: obtaining historical market data of the future varieties, constructing a technical index library, stacking the data into a three-dimensional matrix according to the future varieties, and forming future time sequence data; based on the futures time series data, dividing a training set, a verification set and a test set, carrying out standardization processing on the futures time series data, and constructing a reference data set for futures prediction; constructing a future price prediction model based on the time sequence attention and future variety dynamic graph; and inputting test set data in the reference data set for futures prediction into the trained futures price prediction model to realize prediction of futures prices. According to the method, a plurality of technical indexes are introduced to improve the precision of future price prediction, a multi-time scale aggregation operation is utilized to improve the time sequence sensitivity of a model, the relevance among future varieties is captured by means of a dynamic graph, and finally more accurate future price prediction is provided.

The invention relates to the field of fault detection, in particular to a gear motor fault detection method and system based on vibration signals, and the method comprises the steps: collecting a vibration signal sequence, a load parameter sequence, a rotating speed sequence and a fault label; performing multi-mode decomposition on the vibration signal sequence to obtain subsequences of a plurality of vibration frequency bands, and calculating the contribution degree of a target frequency band; calculating relevance between the target frequency band and the target fault based on the first collaboration and the second collaboration; obtaining the importance weight of the target frequency band in the target fault to obtain weighted subsequences, and taking the sum of all the weighted subsequences as a reconstructed vibration signal sequence of any gear motor; and inputting the obtained three-dimensional matrix of the to-be-detected gear motor into the trained fault detection model, and outputting a predicted value of the to-be-detected gear motor in a target fault. According to the technical scheme, the precision of the fault detection result of the gear motor can be improved.

The application discloses a hyperspectral remote sensing image land cover classification method, system, device and medium, and relates to the field of land cover classification. The method comprises the following steps: acquiring a hyperspectral remote sensing image of land to be classified, and determining a three-dimensional matrix of the hyperspectral remote sensing image of the land to be classified; converting the three-dimensional matrix into a two-dimensional matrix; dividing the two-dimensional matrix of the hyperspectral remote sensing image of the land into a first sample set with real ground object category labels and a second sample set without labels; inputting the first sample set and the second sample set into a weighted space-spectrum joint kernel collaborative representation classification algorithm to obtain a classified hyperspectral remote sensing image; wherein the weighted space-spectrum joint kernel collaborative representation classification algorithm is obtained by introducing a weighted space filtering operator and space structure information into a traditional collaborative representation classification algorithm. The method improves the classification performance of the collaborative representation model and can achieve ideal classification accuracy under small-scale labeled samples.

The application relates to the fields of information security and image processing, and discloses a multi-image parallel encryption processing method based on an improved chaotic mapping, which comprises the following steps: separating and stacking each color channel of multiple images to be encrypted to construct a three-dimensional pixel matrix; extracting a matrix hash digest to be converted into a sub-key; inputting the sub-key into an improved chaotic mapping model of a historical state variable to iteratively generate a scrambling and diffusion sequence; performing sorting scrambling on the three-dimensional matrix in multiple orthogonal dimension sections by using the sequence; mapping the sequence into a shift step length to perform a cyclic shift and a bitwise XOR linkage diffusion on the pixels; and finally, outputting ciphertext images by segmentation. The application breaks the physical isolation of a single image to realize spatial depth mixing by constructing a three-dimensional matrix and performing cross-dimension scrambling; the chaotic dynamics degradation under limited precision is overcome by means of a historical state delay; and the efficiency of multi-image encryption and the system attack resistance are improved by using a shift and XOR linkage to realize bit-level depth confusion.

The application discloses an OFDM high-order modulation signal recognition method based on adaptive data preprocessing and an adaptive wavelet Mamba algorithm, solves how to classify OFDM signals under unknown channel parameters, Doppler frequency shift and complex multipath propagation conditions, and belongs to the field of modulation carriers; the method comprises the following steps: storing OFDM modulation signals as IQ files by simulating actual communication environments through a physical channel simulator; calculating a stable interval of the IQ files by using a steady-state distribution method, cleaning and retaining target samples whose amplitudes meet a threshold; dividing the target samples into sub-blocks, reconstructing the sub-blocks into a structured three-dimensional matrix, and dividing the three-dimensional matrix into a training set and a test set; constructing an AWM from sequentially connected convolution layers, a wavelet transform module, a frequency band perception module, an adaptive gating fusion mechanism module and a fully connected layer; obtaining the trained AWM from the training set; and identifying OFDM high-order modulation signals through the test set; and the application realizes OFDM signal high-order modulation classification.

The invention discloses a line spectrum target detection method based on a vertical array and space-time gain enhancement, and belongs to the field of underwater acoustic detection and array signal processing. According to the invention, the problems of high complexity and poor robustness of the existing array-based processing method are solved. The method comprises the following steps: segmenting a received signal of a vertical array, carrying out DFT (Discrete Fourier Transform) on the segmented signal to obtain a three-dimensional Lofar matrix of the received signal of the vertical array, and respectively calculating a two-dimensional signal matrix and a two-dimensional steering vector corresponding to each group of target motion model parameters in a preset parameter search space; and then motion parameters of the target are obtained based on coherent integration search, passive positioning of the line spectrum target is realized, high space-time gain detection of the line spectrum target is realized through space-time coherent integration, and the detection performance of a weak line spectrum target is improved. The method can be applied to line spectrum target detection.

The present disclosure relates to a method and device for selecting a contrast image sequence and a storage medium, the method comprising: segmenting an aorta region of a CTA contrast image sequence set to obtain an aorta binary mask image sequence set; determining a candidate aorta binary mask image sequence set and a candidate CTA contrast image sequence set according to the aorta binary mask image sequence set and an aorta length threshold; determining a three-dimensional matrix set retaining aorta contrast image information according to each candidate aorta binary mask image sequence and the corresponding candidate CTA contrast image sequence; determining a corresponding reaction tissue density average value of each three-dimensional matrix, and selecting a target CTA contrast image sequence according to the reaction tissue density average value. The purpose of the present disclosure is to automatically screen out a CTA contrast image sequence with better contrast effect from a CTA contrast image sequence set, and improve the screening efficiency of the CTA contrast image sequence.

The application discloses a dynamic fragmentation method of a three-dimensional matrix task, an electronic device and a medium. First, the number of online nodes, the free GPU display memory of the online nodes and the CPU NUMA topology are acquired, and the expected running time and resource demand weight of each task in the three-dimensional matrix task are determined. Then, the three-dimensional matrix task is divided into multiple parts through an improved 0-1 knapsack algorithm based on the number of online nodes, the free GPU display memory of the online nodes, the CPU NUMA topology, the expected running time and the resource demand weight of each task. The dynamic fragmentation method has adaptability, can eliminate long tail delay caused by uneven node load, makes task distribution closer to the theoretical optimum, and further reduces the whole test time consumption by at least 80%, so that the actual utilization rate of heterogeneous computing power resources is close to the theoretical upper limit.

The invention relates to an index type privacy information retrieval method and system for variable-length data retrieval, and the method comprises the steps: carrying out the coding preprocessing of a database through a server, and forming a three-dimensional matrix; the client side calculates a column index and a row index in a database according to the query index; generating a column query ciphertext and a row query ciphertext according to the column index and the row index, and sending the column query ciphertext and the row query ciphertext to a server; the server expands the column query ciphertext, and multiplies and adds the expanded column query ciphertext and the three-dimensional matrix to obtain a first ciphertext array; multiplying and adding the row query ciphertext and the first ciphertext array to obtain a second ciphertext array; and performing rotation fusion on the second ciphertext array to obtain a single ciphertext result, and sending the single ciphertext result to the client to realize privacy information retrieval. Zero-skipping coding and folding retrieval are introduced, force is exerted from two dimensions of data layout and retrieval organization, variable-length entries are compatible, and expenditure is remarkably reduced.