321 results about "Matrix element" patented technology

The invention relates to a high-strength outer plate with excellent flanging forming performance and a preparation method, and belongs to the technical field of steel manufacturing. The high-strength outer plate comprises the following chemical components in percentage by mass: 0.05%-0.14% of C, 0.1%-0.4% of Si, 0.8%-1.4% of Mn, less than or equal to 0.01% of P, less than or equal to 0.005% of S, 0.5%-1.0% of Al, 0.01%-0.04% of Ti and a matrix element Fe, the microstructure of the high-strength outer plate comprises, by area ratio, 80%-90% of ferrite, 5%-15% of martensite and 2%-5% of retained austenite. By designing chemical components and microstructures, ferrite provides good elongation and deformability, martensite improves the strength of the material, and the element Ti realizes grain refinement by forming a precipitated phase, so that not only is the requirement on the waviness of the outer plate favorably realized, but also the hole expansion rate of the material can be improved. According to the obtained high-strength outer plate, the balance of various mechanical properties such as strength, ductility and hole expansion rate is achieved while the surface quality is met, and the forming and performance requirements of the outer plate are met.

The invention provides a self-adaptive beam forming method for a reconfigurable digital array and an IP core. The method comprises the following steps: controlling the starting state of each array element in the array according to an obtained array element state vector # imgabs0 #; according to the # imgabs 1 #, obtaining a row starting vector # imgabs 2 # and a column starting vector # imgabs 3 #, the number of starting rows and columns # imgabs 4 # and # imgabs 5 #, and the number of the central row and column of the effective array area # imgabs 6 # and # imgabs 7 #; according to the # imgabs 8 #, the # imgabs 9 #, the # imgabs 10 # and the # imgabs 11 #, a row interval vector # imgabs 12 # and a column interval vector # imgabs 13 # are determined; recoding the array elements of the effective array area according to the # imgabs14 #, the # imgabs15 # and the # imgabs16 #, and obtaining a mask vector # imgabs17 # of a static weight according to the starting condition of the array elements; obtaining a static wave beam weight vector # imgabs20 # and a static wave beam weight vector # imgabs21 # in the azimuth dimension and the pitching dimension according to the # imgabs18 #, the # imgabs19 # and pre-configured target parameters; according to the # imgabs 22 #, the # imgabs 23 # and the # imgabs 24 #, numbering all the enabled array elements to determine a two-dimensional reconfigurable static wave beam weight vector # imgabs 25 #; storing the obtained signal data of each array element into a target reconfigurable digital array according to a new numbering result, calculating a covariance matrix of the target reconfigurable digital array, and performing inversion; a two-dimensional adaptive beam weight vector # imgabs27 # is determined in combination with # imgabs26 #; and an adaptive wave beam is obtained based on # imgabs28 #, and reconstruction design and flexible editing of array arrangement are realized.

The invention discloses an ore element grade detection method. The method comprises the following steps: respectively obtaining intensity values of X-fluorescence rays absorbed by interference elements and elements to be detected in an ore sample to be detected; inputting the intensity value of the X-ray absorbed by the to-be-detected element into a pre-trained preliminary grade value prediction model to obtain a preliminary grade value of the to-be-detected element; the preliminary grade value prediction model is constructed by using a unary linear regression method; inputting the intensity values of the X fluorescence rays absorbed by the interference elements and the to-be-detected elements into a pre-trained correction compensation model to obtain correction compensation values of the to-be-detected elements; the correction compensation model is constructed by using a dynamic regularization random weight neural network; and taking a sum value of the preliminary grade value and the correction compensation value as a final grade estimation value of the to-be-detected element, so as to obtain a detection result of the to-be-detected ore element grade. According to the method, errors caused by mutual interference among matrix elements are effectively analyzed in an ore dressing field environment, and the accuracy of a detection result is improved.

A hardware accelerator including tiles arranged in a systolic array. At each of the tiles, the systolic array receives a first input block that includes first input matrix elements of a first input matrix. In each of a plurality of multiplication iterations, at each of the tiles, the systolic array receives a respective second input block. The systolic array computes tile products of the first input matrix elements and second input matrix elements included in the second input blocks. The systolic array adds the tile products to column-wise partial sums and transmits the column-wise partial sums to subsequent tiles along accumulator rings included in array columns of the systolic array. In a subset of the multiplication iterations, the systolic array outputs product block rows of a product matrix. The product block rows each include product matrix blocks computed as rows of the column-wise partial sums.

The embodiment of the invention provides a quantification method and reasoning method of an industry large model, equipment and a storage medium. When an original weight matrix of a network layer in an industry large model is quantified, values of matrix elements on diagonals of an original Hessian matrix are obtained based on an input data matrix of the network layer and can represent activation values, and the positions of columns in the original weight matrix and the original Hessian matrix of the network layer are reordered according to an activation value descending mode. Carrying out matrix partitioning processing on the reordered first weight matrix and the first Hessian matrix respectively, and quantizing the first sub-weight matrix based on the quantization parameter of each sub-weight matrix in the first weight matrix in sequence by taking the partitioned matrix as granularity; the method comprises the steps of quantizing a first weight matrix, updating other first weight sub-matrixes which are not quantized based on the quantized first weight sub-matrix, and finally determining a quantization result of an original weight matrix of a network layer based on a quantization result of each first weight sub-matrix in the first weight matrix, so that the size of an industry large model is compressed.

The invention relates to the technical field of electrical digital data processing, in particular to a hardware test resource allocation method based on dynamic topology adaptation, which comprises the following steps of: 1, constructing a dynamic topology sensing network: acquiring electrical connection signals between devices on a test backboard in real time, generating and periodically updating a topology connection matrix, and establishing a dynamic topology sensing network; the matrix element value represents the on-off state of a physical link between devices, and when a device plugging event is detected, matrix updating is triggered immediately; 2, quantifying resource and task attributes; 3, dynamic resource matching of topology driving is executed; and 4, responding to non-interruption migration of topology change: when the topology connection matrix is updated, executing the step 3 again on the influenced task, and realizing non-interruption switching through new and old resource parallel initialization and signal deviation fault-tolerant verification. Through the dynamic topology adaptation technology, test resources can be dynamically adjusted and matched according to real-time topology and task requirements, and efficient utilization of the resources is ensured.

The present application relates to a low-yield ratio 420MPa low-alloy high-strength steel and a preparation method, belonging to the technical field of steel manufacturing. In terms of mass fraction, the chemical composition of the low-alloy high-strength steel includes: C: 0.07% to 0.12%, Si: 0.10% to 0.40%, Mn: 1.60% to 1.80%, P: ≤0.015%, S: ≤0.010%, Alt: 0.020% to 0.060%, Nb: 0.03% to 0.05%, Ti: 0.01% to 0.03%, N≤0.005%, and matrix element Fe. The composition design gives full play to the solid solution strengthening of C and Mn and the fine grain strengthening and precipitation strengthening of Nb and Ti microalloys. At the same time, the continuous full annealing process does not require slow cooling treatment, which improves production efficiency and obtains low-cost, low-yield ratio 420MPa low-alloy high-strength (yield ratio ≤0.8) steel with both strength and excellent plasticity. This type of steel can withstand greater plastic deformation while meeting higher strength requirements without plastic instability, achieving the needs of lightweight automotive industry and excellent stamping integral forming of structural parts, and has broad application prospects.

Systems or methods of the present disclosure may provide an integrated circuit system that includes a programmable logic device that includes a clock, one or more local controllers, programmable logic units implementing a systolic array to compute a matrix multiplication, and embedded memory blocks. The embedded memory blocks include a single port random access memory (SPRAM). The one or more local controllers are configured to, on a first set of alternating clock cycles of the clock, load matrix sub-elements from two rows of a matrix into corresponding matrix element of the SPRAM. The one or more local controllers are configured to, on a second set of alternating clock cycles of the clock, read out the matrix elements from the SPRAM to the systolic array to compute the matrix multiplication.

The invention provides a data transmission method and apparatus, an electronic device and a computer readable storage medium. The method comprises the steps of obtaining a data transmission request sent to a port of a router node; based on a matrix row in a polling matrix matched with the router node, performing polling arbitration on the data transmission request, and determining that a target port of the router node processes data of the data transmission request; updating matrix elements in a matrix row and a matrix column corresponding to the target port, and performing next arbitration; wherein each matrix row represents a port of the router node in one transmission direction; different matrix rows represent different transmission directions, and the matrix elements in each matrix row are used for reflecting the blocking condition of the port corresponding to the matrix row. According to the invention, the arbitration efficiency can be improved, and the stability and fairness of the on-chip internet are improved.

The invention provides a matrix multiplication pipeline calculation method and device, an AI chip, electronic equipment, a medium and a program product, and relates to the field of artificial intelligence, in particular to the field of chips. According to the specific implementation scheme, in response to a matrix multiplication request, a plurality of general calculation components and a plurality of special calculation components in a chip are triggered in parallel, and in each conversion round, the general calculation components obtain matrix element types required by conversion from a first matrix and a second matrix and identify a conversion completion state; and under each calculation round, when the special calculation part queries that each conversion matrix element needing to be calculated in the current calculation round is converted, each conversion matrix element is acquired from the matched general calculation part to execute matrix multiplication calculation. The general calculation part and the special calculation part are executed in parallel, so that the time consumption of data type conversion and the time consumption of matrix multiplication can be mutually masked, the performance of the special calculation part is fully exerted, and the efficiency of matrix multiplication calculation and the performance of an AI chip are improved.

The invention provides a video processing method and device and electronic equipment, and the method comprises the steps: inputting a target video into a coding model, and determining the potential spatial features of each video frame through the coding model; constructing a similarity kernel matrix based on the potential spatial features; determining candidate sub-matrixes from the similarity kernel matrix, and determining the candidate sub-matrix with the maximum element difference among matrix elements in the candidate sub-matrixes as a target sub-matrix; and carrying out dimension reduction processing on the target sub-matrix, and outputting compressed data of the target video based on a dimension reduction result through a decoding model. In the mode, based on the potential spatial features of the target video determined by the encoder, the similar kernel matrix is constructed, representative samples are selected from the similar kernel matrix, dimension reduction is carried out on the samples, and the dimension reduction result is reconstructed again through the decoder, so that more redundant information can be removed on the premise of retaining data information, and the accuracy of the data information is improved. And data processing is performed in the potential space, so that the calculation cost is reduced, and the requirements of reducing storage and calculation resources are met.

Disclosed is an operation device. The operation device includes a memory storing at least one instruction; and a processor configured to execute the at least one instruction, and the processor, by executing the at least one instruction, may perform encoding or decoding for an approximate homomorphic ciphertext using a predetermined matrix having only a half of an element of a matrix corresponding to a canonical embedding function.

The invention relates to an electromagnetic transient modeling and simulation method of a permanent magnet synchronous generator, which can reflect various types of internal faults, and the method comprises the following steps: 1, according to different fault types, establishing mathematical models of the permanent magnet synchronous generator in different fault states, and according to the different fault types, establishing a mathematical model of the permanent magnet synchronous generator in the different fault states, according to the different fault types, establishing a mathematical model of the permanent magnet synchronous generator in the different fault states, and according to the mathematical model, establishing a mathematical model of the permanent magnet synchronous generator in the different fault states, and according to the mathematical model, establishing a mathematical model of the permanent magnet synchronous generator in the different fault states, and establishing a mathematical model of the permanent magnet synchronous generator in the different fault states; 2, focusing matrix elements required to be updated according to fault characteristics, and selecting current or flux linkage as a state variable; 3, performing discretization equivalence on the mathematical equation of the permanent magnet synchronous generator by using a backward Euler integral method, integrating two state variables, unifying the model to obtain an equivalent circuit equation, and constructing an equivalent circuit of the permanent magnet synchronous generator; 4, the equivalent circuit of the permanent magnet synchronous generator is connected with an external electrical network, and the equivalent model carries out node admittance matrix iteration solution on the whole circuit based on an EMTDC solver; and updating other concerned electrical information such as the stator current, the rotor current, the electromagnetic torque, the rotating speed, the rotor angle and the like until the simulation is finished.

The present invention discloses a reconfigurable integrated optical computing core model, comprising: M×N matrix calculation units; each matrix calculation unit includes two dual-waveguide microring resonators, a straight waveguide, and a phase change material heater, and the straight waveguide is made by covering a phase change material on a silicon waveguide; the phase change material heater is used to heat the phase change material to generate a phase change, thereby changing the light absorption of the straight waveguide; the integrated optical computing core model realizes the multiplication operation of an M×N matrix and an N×1 vector through integrated optical methods, where M and N are natural numbers. The present invention uses a phase change material in the computing core, changes the phase state of the phase change material by heating, changes the light absorption of the straight waveguide, and further changes the matrix elements in the computing core, realizing the reconfigurable characteristics of the computing core model. The present invention can be widely applied to the field of integrated optics.

The invention provides a production line parameter real-time scheduling method based on reinforcement learning, and belongs to the technical field of production lines, and the method comprises the steps: collecting sensor data to form an original state vector, reducing the dimension of the original state vector into a low-dimensional feature state vector through a state compression encoder, inputting the low-dimensional feature state vector corresponding to a microcosmic scheduling unit into a parameter decision model, and carrying out the real-time scheduling of the microcosmic scheduling unit; the attention weight coefficient of the model is determined by the product of the historical scheduling success rate, the element value of the difference degree matrix and the maximum characteristic value of the inter-stage sensitivity matrix, after a scheduling instruction is output, simulation evaluation is performed in a digital twin platform, and after the scheduling instruction passes, a real production line is issued for execution and multi-time-scale deviation indexes are collected; and calculating a comprehensive reward value according to the indexes, and storing an experience sample to an experience playback buffer area for model online update training, thereby solving the technical problem that production line parameter scheduling is difficult to consider multi-level state feature recognition and dynamic decision weight optimization at the same time.

A processing apparatus is described herein that includes a general-purpose parallel processing engine comprising a matrix accelerator including one or more systolic arrays, at least one of the one or more systolic arrays comprising multiple pipeline stages, each pipeline stage of the multiple pipeline stages including multiple processing elements, the multiple processing elements configured to perform processing operations on input matrix elements based on output sparsity metadata. The output sparsity metadata indicates to the multiple processing elements to bypass multiplication for a first row of elements of a second matrix and multiply a second row of elements of the second matrix with a column of matrix elements of a first matrix.

The invention discloses a method for constructing a basin water resource scheduling model based on a formalized matrix. The method comprises the following steps: dividing calculation units; constructing a drainage basin water cycle scheduling matrix, and defining a calculation unit as a matrix element; unit water circulation calculation: predicting interval incoming water and industry water demand of each calculation unit, and simulating a water consumption and drainage taking process based on incoming water prediction, industry water demand and engineering water supply capacity of the calculation units; determining a single-reservoir water supply scheduling rule of the reservoir; and finally, according to the drainage basin water circulation scheduling matrix, simulating a drainage basin water resource scheduling process, and outputting a water demand satisfaction rate of each water consumption unit and a drainage basin important monitoring section flow guarantee rate. Therefore, the drainage basin water circulation scheduling matrix is used for representing the spatial layout of drainage basin water resource scheduling, drainage basin water resource scheduling is effectively achieved, meanwhile, visualization of drainage basin water resource scheduling model results can be enhanced, and the working efficiency of drainage basin water resource scheduling research workers is improved.

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. In accordance with an aspect of the disclosure, a method for minimizing noise and interference in a communication network is provided. The method comprising: estimating channel based on reference signals transmitted by user equipment (UEs) and plurality of received signals, determining noise and interference sample based on estimated channel, generating noise and interference co-variance matrix based on noise and interference sample, generating an extended channel matrix including estimated channel and at least part of noise and interference covariance matrix based on the number of UEs, generating resultant signal based on extended channel matrix and plurality of received signals, and performing, based on resultant signal, equalization by applying equalization matrix across extended channel matrix. The present disclosure eliminates intra-cell and inter-cell interferences, reduces matrix elements, fronthaul load, computational complexity.

Embodiments of apparatuses and methods for copying and operating on matrix elements are described. In embodiments, an apparatus includes a hardware instruction decoder to decode a single instruction and execution circuitry, coupled to hardware instruction decoder, to perform one or more operations corresponding to the single instruction. The single instruction has a first operand to reference a base address of a first representation of a source matrix and a second operand to reference a base address of second representation of a destination matrix. The one or more operations include copying elements of the source matrix to corresponding locations in the destination matrix and filling empty elements of the destination matrix with a single value.

A computing system for performing runtime data handling optimization for generative models is provided. The computing system comprises at least one processor and memory comprising a first memory and a second memory, wherein the memory stores instructions that, when executed by the at least one processor, cause the at least one processor to execute a generative model. The computing system computes a first value matrix entry based upon the processing of an input to the generative model. The first value matrix entry is stored in a first memory wherein a first group of value matrix entries is identified. The computing system executes data quantization on the first group of value matrix entries which results in a first quantized value matrix. The first quantized value matrix is added to a second memory where it can be used during generation of the generative model. When the first group of value matrix entries is less than a group size parameter, data padding matrix values are generated and used during execution of the data quantization.

A high-impedance node detection method and system, and a circuit simulation device and a readable storage medium. The high-impedance node detection method comprises: acquiring a conduction matrix generated in a circuit simulation process, wherein each row and each column of the conduction matrix respectively correspond to one node, and when a matrix element is a first identification value, this indicates that a node corresponding to the row where the matrix element is located is connected to a node corresponding to the column where the matrix element is located (S110); on the basis of the conduction matrix, dividing mutually connected nodes into the same group, and respectively performing separate grouping on nodes that do not have connected nodes, so as to obtain a plurality of groups of nodes (S120); and among the plurality of groups of nodes, determining at least one group of nodes comprising a reference node to be a non-high-impedance group, and determining the other groups of nodes to be high-impedance groups (S130). The detection method can quickly identify high-impedance nodes of a simulated circuit at various simulation moments, thereby reducing the computational workload in a high-impedance node detection process, improving the detection efficiency, and shortening the detection time.

A sparse dot product and / or matrix multiply is computed by subdividing each vector and simultaneously performing operations to generate output matrix elements. In an embodiment, a bit mask is computed that includes one bit for each element of an input matrix, each bit indicating whether the element is non-zero or zero. In an embodiment, the element values are stored in a packed format, where all of the non-zero values are packed together and the remaining storage for the matrix contains zeros (or any other values). The bit mask can then be used to determine the location of each non-zero element in the packed storage. Rather than reading all of the elements, only the non-zero elements that will be multiplied by a non-zero element from the other input vector should be read. Any multiplication by a zero element from either input vector or matrix is unnecessary.

The invention relates to 750MPa-grade high-strength steel with high comprehensive formability and a preparation method, and belongs to the technical field of steel preparation. The high-strength steel comprises the following chemical components in percentage by mass: 0.04%-0.08% of C, less than or equal to 0.2% of Si, 1.4%-2.0% of Mn, less than or equal to 0.01% of P, less than or equal to 0.005% of S, 0.02%-0.05% of Al, 0.02%-0.06% of Nb, 0.08%-0.15% of Ti, 0.1%-0.3% of Mo, less than or equal to 0.004% of N and a matrix element Fe, the microstructure of the high-strength steel comprises, by volume fraction, 60%-80% of ferrite and 20%-40% of bainite. Through reasonable component design, a metallographic structure of a target proportion is obtained, so that the obtained high-strength steel has relatively high elongation and hole expansion rate, and has excellent comprehensive forming performance.

A Winograd algorithm multiplies a first matrix by a second matrix to generate a third matrix. A plurality of second accumulated value calculation units, accumulates second multiplication values obtained by multiplying each of paired element values of the second matrix, a second accumulated value output unit outputting selecting and outputting one of output values of adjacent second accumulated value calculation unit and an accumulated second multiplication value as a second accumulated value, a third accumulated value output unit including a plurality of third accumulated value calculation units and generating third accumulated value, and one or more row element value calculation units, of which one row element value calculation unit accumulates first matrix element multiplication values obtained by multiplying each of the paired element values of the first matrix, and calculates element values of the third matrix using the accumulated first matrix element multiplication value and the second accumulated value.

The disclosed embodiments relate to matrix compression / decompression instructions. In one example, a processor includes fetch circuitry to fetch a compression instruction having a format with fields to specify positions of an opcode and a decompressed source matrix and a compressed destination matrix; the decoding circuit is used for decoding the extracted compression instruction; and execution circuitry to, in response to the decoded compression instruction, generate a compressed result according to a compression algorithm by compressing a specified decompressed source matrix via any of: compress non-zero-value elements together and store a matrix position of each non-zero-value element in a header, or using fewer bits to represent one or more elements and using a header to identify matrix elements represented by fewer bits; and storing the compressed result to the specified compressed destination matrix.

The application relates to a power data fusion method, comprising the following steps: acquiring time series data from multiple data sources respectively; calculating the similarity between each time series data to obtain a similarity matrix; accumulating the matrix elements in the same row or the same column in the similarity matrix to obtain the similarity accumulation value of each data source and other data sources; performing longitudinal parameter fusion on each data source with a similarity accumulation value higher than a first threshold value, comprising the following steps: performing data preprocessing on the time series data; calculating the grey correlation degree between each two time series data of the time series data after the data preprocessing; filtering out the time series data with a grey correlation degree lower than a third threshold value; performing data fusion on the time series data by using a BP neural network to obtain a first fusion result; and performing horizontal parameter fusion on each data source with a similarity accumulation value lower than the first threshold value.

The present invention proposes a method for solving the transmission characteristics of a cellular frequency selective surface. The implementation steps include: constructing a unit geometric model of the cellular frequency selective surface; performing mesh division on the unit geometric model; calculating the direction vector of the polarized plane wave in OXYZ; calculating the incident field at the excitation port in different modes; constructing a finite element matrix equation; performing matrix element substitution on the matrix equation; and obtaining the transmission characteristics of the cellular frequency selective surface. By using two vector edges of the excitation port and transforming the three-dimensional coordinate system into a three-dimensional local coordinate system, and calculating the wave numbers of the polarized plane wave in the three axial directions of the three-dimensional local coordinate system, the present invention calculates the incident field at the excitation port in different modes, avoiding the defect in the prior art that the incident field at the excitation port cannot be calculated in a coordinate system with two vector edges of a regular hexagon as the coordinate axes by using the finite element method, and achieving high-precision solution of the transmission characteristics of the cellular frequency selective surface.

This method, belonging to the field of electromagnetic engineering, aims to improve the speed of impedance matrix filling when solving electromagnetic scattering fields of rotating bodies. Based on a higher-order Taylor series method, it obtains more accurate impedance matrix elements with less sampling data, thus significantly saving computational resources and improving design efficiency. Simulations of various rotating electromagnetic devices demonstrate the superior performance in terms of impedance matrix filling accuracy, speed, and memory consumption, as well as its universality for different models. The method derives the modal Green's function expression after higher-order integration and performs simulations using specific electromagnetic rotating device models. The increased order of the method has practical application value in improving the accuracy of device model simulations. The higher-order numerical integration method also brings efficiency improvements, saving memory consumption and significantly increasing the impedance matrix filling speed compared to the traditional T-shaped integration method.

The invention relates to a low-carbon-emission high-performance IF steel plate and a preparation method, and belongs to the technical field of steel preparation. The IF steel plate comprises the following chemical components in percentage by mass: less than or equal to 0.0030% of C, less than or equal to 0.0060% of S, less than or equal to 0.030% of Si, less than or equal to 0.2% of Mn, 0.0030%-0.006% of N, 0.060%-0.12% of Al, 0.030%-0.080% of Ti, 0.0015%-0.030% of Nb, less than or equal to 0.0008% of B, less than or equal to 0.12% of Cu, less than or equal to 0.01% of Sn, less than or equal to 0.15% of Cr, less than or equal to 0.08% of Ni, 0.001%-0.02% of La + Se and a matrix element Fe, al / N is greater than or equal to 18 and less than or equal to 22, Ti is greater than or equal to 3.4 N, Nb is greater than or equal to 7.75 C, and Al / N represents the mass fraction ratio of Al to N. By accurately controlling the content of elements in steel, formation of a precipitated phase and distribution of grain boundary elements are controlled, and the influence of residual elements in waste steel and electric furnace nitrogen increase on the performance of a finished product is reduced. The short-process production of IF steel by the full-scrap steel electric furnace is realized, the production cost and carbon emission are reduced, and meanwhile, the high performance of the material is ensured.

The invention discloses a digital Kalman gain rapid calculation method for a time-sharing uniformly-accelerated radar target, which comprises the following steps of: further coupling a Kalman gain calculation formula at the kth moment and a state one-step prediction covariance equation at the (k-1) th moment by extracting the characteristics of a prediction equation and an update equation in Kalman filtering, and calculating a matrix HPk in advance to obtain the Kalman gain of the kth moment. And the k-1HT + Rk achieves an optimization effect. According to the method, the distribution rule of inverse matrix elements of a matrix HPk, k-1HT + Rk is deeply mined by utilizing the mathematical characteristics of the matrix HPk, k-1HT + Rk, so that the rapid calculation of the inverse matrix is realized. The improvement greatly improves the calculation speed of the Kalman gain. Compared with a traditional Kalman filtering algorithm, the method shows greater potential in the aspect of parallel calculation, the calculation complexity is effectively reduced, and the execution speed of the Kalman filtering process is remarkably increased. According to the invention, a more efficient solution is provided for real-time tracking and prediction of the space target, and the method has significant advantages especially in a scene where computing resources are limited or high-frequency updating is needed.