257 results about "Adaptive computing" patented technology

The invention discloses an end-side cloud collaborative intelligent fire-fighting fire monitoring system based on image fusion, and relates to the technical field of intelligent fire-fighting, the system is composed of a plurality of functional modules, and the system comprises a multi-modal image fusion module which generates a dynamic scanning priority map based on prior data, distinguishes a natural heat source from an abnormal fire by using a dual-light fusion algorithm, and sends an image fusion result to a cloud server; a scanning area is divided according to the thermal risk grade, and the thermal imaging resolution is dynamically adjusted; the distributed edge computing module is used for carrying out space-time synchronization on cross-modal data through a multi-modal feature alignment network, and carrying out dynamic allocation on a CUDA core and CPU resources through adaptive computing scheduling; an improved artificial bee colony algorithm is adopted, the bandwidth of the multi-sensor data flow is dynamically allocated through a time-sharing multiplexing protocol, and three-dimensional path planning is carried out; and the end-side cloud collaborative decision module constructs a federated learning driven model sharing network, and each edge node trains a lightweight YOLOv5s pruning model based on local data.

The invention provides a multifunctional inspection device detection system based on a distribution network mobile operation terminal, and relates to the technical field of electric power detection, and the multifunctional inspection device detection system comprises a portable main terminal of a dynamic collaborative architecture, a wearable terminal for augmented reality interaction and a distributed heterogeneous sensing cluster, which form a closed-loop data chain through an electric power dedicated low-delay wireless communication network; the portable main terminal is integrated with a heterogeneous computing unit with adaptive computing power distribution, can perform real-time fusion analysis on multi-dimensional sensing data, and outputs a visual result containing fault location and confidence; the wearable terminal superposes fault information to a real scene in a three-dimensional marking form through a virtual-real fusion positioning technology, and supports eye movement and voice collaborative interaction; multi-module collaborative acquisition of the distributed heterogeneous sensing cluster is combined with a nanosecond timestamp synchronization mechanism, so that the limitation of traditional single parameter detection is broken through, rich and synchronous basic data is provided for subsequent analysis, and the comprehensiveness of routing inspection is greatly improved.

The invention relates to the technical field of prediction algorithms, and discloses a high-precision active power filtering and prediction algorithm for a three-phase ammeter, which comprises the following steps: acquiring original signals of three-phase voltage and current for baseline correction, performing time-frequency analysis on non-stationary harmonic waves and noise of the original signals, and dynamically adjusting filtering parameters; voltage and current phase alignment is carried out through FIR phase shift and zero crossing point detection, a phase-locked loop is constructed by using GRU to track the frequency of a power grid, the sampling frequency is dynamically adjusted, and frequency mutation is detected; iapFFT transformation is optimized to suppress spectrum leakage, a phase error is corrected through a deep learning network, and weak harmonic detection is enhanced by using an attention mechanism; classifying harmonic waves, and adaptively calculating total active power; dimensionality reduction is carried out on historical power and environmental parameters through an auto-encoder, and future active power is predicted; low-power-consumption hardware is adapted, and training efficiency and data security are optimized; three-phase signals are processed in parallel, and FPGA storage and FFT / FIR cores are optimized for filtering processing; and dynamically adjusting parameters of the phase-locked loop.

The invention provides an automatic history fitting method for gas drive numerical reservoir simulation, which comprises the following steps of: 1, establishing a gas drive numerical simulation mathematical model, and correcting the gas drive numerical simulation mathematical model by using a gas drive PVT phase state fitting experiment; 2, solving the corrected gas drive numerical simulation mathematical model obtained in the step 1, performing parameter analysis, and screening main control factors influencing accumulated oil production; 3, generating a numerical simulation training sample set based on an orthogonal experiment, and obtaining the main control factor parameter initial value constraint screened out in the step 2 through a machine learning algorithm; step 4, establishing a multi-parameter fitting objective function for realizing historical fitting; and step 5, using the main control factor parameter initial value constraint obtained in the step 3, performing iterative calculation through a projection gradient method, fitting the target function established in the step 4, and terminating when a convergence condition is satisfied. According to the method provided by the invention, the constraint parameter values can be adaptively calculated, automatic historical fitting is completed, and the definition of the fitting parameter range does not depend on empirical and experimental determination any more.

The invention provides a self-adaptive sampling method for fault diagnosis under data multi-class imbalance and related equipment, and effectively solves the problems of low sample generation quality, high parameter dependence, high calculation complexity, poor adaptability to complex working conditions and the like. The method comprises the steps of coping with different data feature scenes through a parameter adaptive calculation mechanism, then exploring a global optimal solution in multi-classification modeling accuracy model solution optimization by an evolution mechanism through employing a Newton-Raphson optimizer thought, and finally converting an optimal solution set into various fault samples by using a feature recombination mechanism. Therefore, high-quality sample equalization is realized, small sample multi-class imbalance fault diagnosis is carried out by combining MAESTE and multi-class LS-SVM, and the interpretability of the model is improved.

The invention discloses a dynamic calculation distribution and error control acceleration system and method for a diffusion model, and the system comprises a feature prediction module, a verification decision module, a sample complexity analysis module, and an interface and integration module. On the premise of ensuring that the generation effect is basically unchanged, the number of times of complete forward calculation is remarkably reduced, so that while the overall reasoning efficiency of the generative artificial intelligence task is improved, complete forward calculation is executed in a plurality of key time steps, and high-precision features are obtained to serve as a reference; and then a feature prediction module is used to combine feature difference information of the current time step and a plurality of previous time steps, and rapid prediction of features of a plurality of subsequent time steps is realized through a Taylor expansion formula. A prediction result is not directly adopted, but is submitted to a lightweight verification decision module for error detection and reliability evaluation; and the module judges whether to accept a prediction result or execute complete calculation in a back-off manner according to a verification result and a dynamically adjusted error threshold value, so that reasonable allocation of calculation resources among samples with different complexities is realized, and error accumulation is effectively inhibited while the reasoning speed is improved.

The invention discloses a storage and calculation separation method and device based on multi-level cache and intelligent scheduling and a server, and belongs to the technical field of data processing, and the method comprises the following steps: collecting operation behavior data of a user, carrying out distributed storage, and dynamically distributing and calculating node cache space; performing classification marking to form marked user data; performing intelligent scheduling layer analysis tasks on the marked user data, and screening and distributing adaptive computing nodes; the computing node receives the analyzed task to check the local cache, obtains corresponding data from the local or request remote storage according to the validity of the local corresponding data, computes the corresponding data, and caches the computed data to the local or remote storage according to the user access probability; and carrying out dynamic scheduling and hierarchical storage on the calculated data between local cache and remote storage according to access frequency and heat factors. According to the invention, the problems of low transmission efficiency and unbalanced resource utilization of the existing storage and calculation separation architecture are solved.

Aspects of embodiments pertain to a method for determining an Input / Output (I / O) device configuration for a music teaching system. The method may comprise receiving a plurality of I / O device configurations of a music teaching system; receiving, for a given I / O device of the music teaching system, an initial I / O device configuration; and determining an updated I / O device configuration for the given I / O Device, based on the initial I / O device configuration of the given I / O Device and at least one of the plurality of received I / O device configurations. The determining is performed such that the updated I / O device configuration has improved device performance compared to the initial I / O device configuration.

The invention discloses a quantum photon hybrid interface adaptive calculation method, and relates to the technical field of quantum calculation and photon integration. Firstly, the current load state of the multi-modal data interface is monitored in real time, and an interface state feature matrix is obtained; then inputting the state matrix into a quantum photon hybrid calculation module, and optimizing an interface type, a transmission protocol and interface parameters in real time through a quantum state feedback and quantum variational optimization algorithm (QAOA) to obtain optimal adaptive configuration of the interface; and dynamically adjusting interface parameters according to an optimization result to realize that the random read delay is less than or equal to 40 microseconds, the sequential read delay is less than or equal to 25 microseconds, the write delay is less than or equal to 400 microseconds, the target identification response time is less than or equal to 0.5 ms, and the target tracking response time is 0.5-1 second. The dynamic intelligent optimization of the interface parameters is realized, the interface transmission efficiency and the real-time response capability are remarkably improved, and the method is suitable for the fields of artificial intelligence, intelligent transportation and the like.

The invention discloses a seamless switching method and system for a high-speed terminal of a ground-non-ground convergence network based on trajectory prediction and resource pre-reservation, and belongs to the technical field of space-ground convergence communication. The core of the method is that a switching area is accurately judged through bidirectional motion prediction of a terminal and an NTN node; a dynamic switching window is calculated in a self-adaptive mode by combining factors such as terminal speed and network time delay; intelligently selecting an optimal target link by using an AI enabled link scoring model; and cooperative pre-reservation and uplink and downlink synchronization of resources are completed before the window. The method has the advantages that the problem of failure of traditional RSRP judgment in an NTN dynamic environment is solved, the switching success rate and prediction accuracy in a high-speed moving scene are remarkably improved, the service interruption time and signaling overhead are greatly reduced, the continuity and reliability of communication are effectively guaranteed, and the method is suitable for large-scale popularization and application. The method is suitable for high-dynamic service scenes such as intelligent network connection vehicles and unmanned aerial vehicles in a 6G network.

The invention discloses an intelligent manufacturing workstation safety control system oriented to man-machine cooperation, and belongs to the technical field of intelligent manufacturing and man-machine cooperation. The system comprises a multi-modal sensor array, a personnel state sensing module, a robot state monitoring module, a dynamic safety area adaptive calculation module, an intention prediction and risk assessment module, a cooperative control decision module and an execution feedback module. The dynamic safety area self-adaptive calculation module constructs geometric representation of a man-machine interaction space based on a topological manifold theory, and calculates a dynamic safety boundary in real time; the intention prediction and risk assessment module is fused with multi-source information to predict a future trajectory of a person and assess a collision risk; the cooperative control decision module performs multi-objective optimization with safety and efficiency as double objectives to generate an optimal adjustment strategy, the system realizes prospective safety control based on intention prediction, the cooperative efficiency is improved by 25%-45% on the premise of guaranteeing safety, and the working safety accident rate is reduced to be close to zero.

The invention discloses a multi-source fusion carbon emission factor adaptive calculation system and method, and relates to the technical field of carbon emission management, and the system comprises a data input module, a sub-model module, a total model integration generation module, a model weight adjustment module and a carbon emission factor calculation system module. The sub-model module can generate a calculation model of the four sub-models according to a fuel type, a region, a time sequence and a process type, and the total model integration generation module integrates the four sub-models and generates a Stacking integrated prediction large model. According to the method, four sub-models can be generated according to the known fuel, region, time and process information, the four sub-models can be integrated into a large judgment model, the large judgment model can be applied to calculate the result according to the characteristics of different industries and different scenes through the large judgment model, and the prediction precision can be improved in a large range.

The invention discloses a short temporary rainfall forecasting method and system based on a dynamic neural network architecture. According to the method, training sample distribution is optimized through a space-time gradient driven resampling strategy, a time-frequency double-branch dynamic network architecture is adopted, a time domain branch extracts multi-scale space features through an encoder-decoder structure, a frequency domain branch adaptively activates an FFT calculation module through a content awareness dynamic network to capture multi-scale time features, and the multi-scale time features are obtained. And two key problems of meteorological data distribution imbalance and deep learning model optimization imbalance are solved in combination with a balance regression loss function. According to the method, adaptive computing resource allocation for meteorological events with different complexities is realized, the forecasting precision and computing efficiency of heavy rainfall events are remarkably improved, and an efficient and reliable technical solution is provided for short temporary rainfall business forecasting.

The invention discloses an industrial Internet of Things federated learning method based on a federated increment decision tree, and the method comprises the steps: a cloud server deploys and initializes a federated learning global model and the federated increment decision tree, and sets a statistical histogram bucket boundary set of each feature value; in each federated learning iteration, the cloud server broadcasts a federated learning global model parameter, each industrial device adopts a local data set to train and count to obtain a local gradient histogram parameter, the edge server performs local aggregation on the local gradient histogram parameter and the federated learning model parameter, and the edge server performs local aggregation on the local gradient histogram parameter and the federated learning global model parameter; and the cloud server globally aggregates the local aggregation parameters of the gradient histogram and then incrementally trains the federated increment decision tree, simultaneously aggregates the parameters of a federated learning global model, and adaptively calculates an aggregation weight based on a second-order gradient value during local aggregation and global aggregation of the parameters of the federated learning model. The federal learning overhead can be effectively reduced, and the performance of the federal learning model is improved.

The invention discloses a tunnel oxygen supply control method based on multi-parameter coordination, and relates to the technical field of tunnel construction, and the method comprises the steps: constructing a multi-source environment monitoring data set, training a target ventilation quantity prediction model fused with a high-altitude correction coefficient based on the data set, and outputting an initial target ventilation quantity and an oxygen supply parameter; generating a pre-regulation instruction based on the initial target ventilation quantity and the oxygen supply parameters, simulating an instruction execution process through a digital twin virtual simulation model, and outputting oxygen supply evaluation parameters of oxygen content distribution, wind pressure loss and energy consumption of each block of the tunnel; constructing a tunnel oxygen supply evaluation model, and comparing oxygen supply evaluation parameters obtained by simulation with a safety threshold; and executing the formal adjustment instruction. Through multi-dimensional sensing treatment, high-altitude self-adaptive calculation, digital twinborn simulation verification, multi-scene parameter optimization, multi-device collaborative linkage and model dynamic evolution technologies, the breakthrough of tunnel oxygen supply from static experience control to dynamic precise intelligent regulation is realized.

The invention discloses an adversarial unsupervised time sequence anomaly detection method and system based on a KAN, and belongs to the field of industrial time sequence data anomaly detection. The method comprises the following steps: (1) acquiring a training set and a test set composed of label-free industrial time sequence window data; (2) training a shared encoder and double decoder model based on KANs by using the training set, and adaptively calculating an abnormal score threshold value of the model by using the test set; and (3) deploying the trained model on an actual industrial production line, calculating an abnormal score of industrial time sequence window data collected by an industrial sensor in real time, and if the abnormal score exceeds an abnormal score threshold, determining that the data in the window is abnormal. The method has the advantages of excellent high-dimensional data representation capability, strong anti-noise performance, high-quality anomaly detection capability and good portability without depending on priori knowledge under multiple industrial backgrounds.

The invention provides a service interface dependency relationship mining and analysis method based on AI and graph calculation, and relates to the technical field, and the method comprises the steps: constructing a dependency relationship graph, guiding random walk sampling through a meta path, adaptively calculating the radius of a dynamic sub-graph, and hierarchically detecting a framework. The detection accuracy of the abnormal dependency relationship is improved, the operation and maintenance cost of the micro-service system is reduced, and the stability and reliability of the system are enhanced.

The invention provides an unmanned aerial vehicle pose estimation method based on credibility weighted strong tracking filtering. According to the method, the posterior error covariance after actual strong tracking filtering iteration and the theoretical true posterior covariance of algorithm iteration are utilized to define a trust factor used for describing filtering deviation under noise uncertainty, the trust factor acts on self-adaptive calculation of a forgetting factor, then a time-varying fading factor is corrected through calculation of an innovation error covariance, and therefore the filtering deviation under noise uncertainty is corrected. And tracking estimation under the condition of noise uncertainty is realized. Compared with traditional strong tracking series filtering, the method can quantify the uncertainty of noise and relieve experience dependence of forgetting factors. Meanwhile, the time-varying fading factor acts on the process noise covariance, and compared with a traditional action mode, the method has better interpretability. When the residual error and the fading factor are calculated, compared with the traditional fixed forgetting factor and the trust factor, the self-adaptive adjustment of the forgetting factor can be realized, the performance error is reduced, and meanwhile, the artificial experience assignment is reduced.

The invention discloses an unmanned aerial vehicle performance picture generation method, device and equipment and a medium, and the method comprises the steps: directly generating a line draft image according with an unmanned aerial vehicle performance specification through a multi-modal generation model trained based on a special data set according to text or image input. And carrying out structural analysis on the line draft, and separating a contour skeleton from an internal filling area. In the point distribution stage, key inflection points of the contour are innovatively recognized and locked as fixed anchor points, and it is ensured that geometric features are not lost in follow-up processing; and combining binary search to adaptively calculate an optimal spacing, and applying constraint space relaxation in a filling area. And finally, through local security assessment, minimum addition and deletion are carried out on point locations only on the security line segments, and the target sortie is accurately matched. The problem of low production efficiency caused by excessive manual operation is effectively solved, and the mass production requirement of high-frequency commercial performance is fully met.

The invention discloses a rolling schedule self-adaptive calculation method and system based on a neural network, relates to the technical field of metallurgical industry automation and intelligent manufacturing, and solves the technical problem that self parameters are difficult to adjust and update by constructing a double neural network model according to newly generated incoming material data and an actual rolling schedule. Comprising the following steps: collecting incoming material data, performing logarithmic transformation on thickness data, introducing a thickness change rate as an input feature, then constructing a first neural network to predict the number of rolling passes, calculating a reduction rate and a tension parameter of each pass by a second neural network, and embedding a model into a production system to generate a rolling schedule in real time. And finally, new data and historical samples are integrated, a model is continuously optimized through data set construction, offline training and online learning, a Bayesian updating rule is introduced in an online learning stage, model parameters are dynamically adjusted in combination with the new data and historical knowledge, and self-adaptive optimization of the rolling schedule is achieved.

The invention relates to the technical field of medical image processing and surgical navigation, in particular to a liver segmentation, modeling and surgical simulation system based on a cascade neural network and a multi-scale reconstruction technology. The multi-scale dynamic registration module is used for carrying out coarse segmentation and fine segmentation on liver and focus areas of the medical image and is used for non-rigid registration and respiratory motion compensation of the multi-modal medical image; the physical enhancement modeling engine is used for constructing a liver vessel tree three-dimensional model containing hemodynamic characteristics; the operation navigation and risk assessment module is used for performing real-time navigation and operation path planning in the operation; and the self-adaptive computing framework provides underlying computing resource support for the system. And high-precision segmentation: through a cascade recursive network and a boundary sensitive loss function, the liver tumor segmentation Dice coefficient reaches 94-96%, which is increased by 6-12% compared with the traditional method.

The invention discloses a single-channel non-real-time speech enhancement method based on a Taylor model, a computer storage medium and a product. The method comprises the following steps: S1, collecting and preprocessing noisy speech data; s2, constructing a novel TaylorSENet neural network model, wherein the model comprises a zero-order block, a high-order block and a self-adaptive order selection module; s3, enhancing the noise-containing voice data preprocessed in the step S1 by using the model in the step S2, and outputting an enhanced complex spectrum; and S4, the novel TaylorSENet neural network model in the S2 is trained until the learning rate is kept unchanged. According to the invention, by constructing the novel TaylorSENet neural network model and through multi-scale feature coding and self-adaptive calculation order selection, the high speech enhancement performance is maintained, the calculation complexity and the reasoning time are significantly reduced, and calculation resources can be intelligently allocated according to the input signal-to-noise ratio.

The invention relates to the technical field of data processing, and discloses an urban space structure collaboration measurement method and system, and a storage medium. The method comprises the following steps: performing gridding processing on urban space elements through multi-temporal data acquisition to obtain a three-dimensional data matrix; calculating a space-time dynamic coupling coefficient by adopting an improved gravity model according to the three-dimensional data matrix; constructing a space-time bidirectional weighting network through a bidirectional weighting algorithm; a dynamic collaboration degree index DSCI is calculated based on network self-adaption; and determining a collaboration degree threshold value through multi-level nesting identification, and obtaining a measurement report. According to the method, through constructing the space-time bidirectional weighted network and the dynamic coordination degree index DSCI adaptive calculation algorithm, the space-time dynamic coupling relation quantification precision of the urban space structure coordination measurement is improved. Meanwhile, a self-adaptive collaboration degree threshold combination is established through multi-level nesting recognition, and the dynamic adaptability of the collaboration evaluation standard to the urban development stage and policy environment change is improved.

The invention relates to the technical field of earthwork measurement, in particular to an accurate earthwork measurement system based on a laser radar technology. The system comprises a mobile scanning acquisition module, a point cloud intelligent processing module and an engineering quantity accurate calculation and visualization module. The mobile scanning acquisition module dynamically acquires original three-dimensional point cloud data of a construction area through a laser radar; the point cloud intelligent processing module separates ground point cloud from the complex scene point cloud with high precision through a dynamic filtering algorithm; and the project amount accurate calculation and visualization module accurately calculates the total amount of filling and excavation through a boundary adaptive calculation method. According to the invention, a dynamic filtering algorithm and a boundary adaptive calculation method fusing geometric features and physical attributes are provided, and full-automatic, high-precision and visual measurement of the earthwork volume is realized.

The invention provides a welding seam parameter anti-interference measurement method, which comprises the following steps of: acquiring spectrum characteristics of a high-reflectivity surface through a laser scattering technology and Fourier transform processing, matching a correction coefficient, realizing weighted adjustment and geometric parameter extraction of a scattering signal, accurately quantifying a groove size in combination with an edge enhancement algorithm and a three-dimensional contour modeling technology, and accurately measuring the welding seam parameters. Interference such as surface oil stain, corrosion and stripe fracture is effectively overcome by using image filtering, morphological reconstruction and an abnormal value elimination algorithm, and finally, high-precision and anti-interference measurement of the weld groove angle, the truncated edge thickness and the assembly alignment tolerance under high-reflection, corrosion and complex working conditions is realized through multi-source data fusion and self-adaptive calculation range adjustment. And the reliability and the automation level of welding quality detection are remarkably improved.

The invention provides a Flink multi-cluster security authentication data synchronization method and system, and relates to the technical field of distributed computing, and the method comprises the steps: establishing a secure connection channel, optimizing an execution plan by adopting an incremental coding compression algorithm, setting an adaptive computing load threshold, dynamically allocating computing resources, and writing state check point information. Data quality evaluation is executed, the evaluation data is written into the distributed account book, the state of the target cluster is recovered, the synchronization task is started, the data synchronization safety and efficiency are improved, the computing resource utilization rate is increased, and the data quality and synchronization consistency are ensured.

The invention discloses a secure closed-loop circulation and physical output control method and system for an oversized raster image. The method comprises the following steps: a client adaptively calculates fragmentation granularity according to a network state, fragments an image and encrypts and transmits the image; the server performs secondary encryption and distributed storage on the received data; the preview end constructs a multi-resolution hierarchy based on a viewport mapping technology, only extracts visual slices and superposes watermarks, and transmits the visual slices and the watermarks to a client memory for zero-cache rendering; and the printing end constructs a secure channel bound with a hardware fingerprint, carries out streaming decryption on the data, embeds traceability information, and injects a printing drive in real time. According to the method, the problems of unstable GB-level image transmission and unsmooth preview are solved, and high-efficiency circulation and strict protection of design assets are realized through full-process data non-landing streaming control.

The invention discloses an element size effect elimination method for concrete structure damage finite element simulation, which comprises the following steps of: 1, performing finite element mesh generation on a concrete structure, endowing all elements with a concrete damage zone model based on a power law type tensile damage equation and initial model parameters, and establishing a concrete structure damage finite element model; step 2, obtaining size information of each unit in the concrete structure damage finite element model in the step 1, and adaptively calculating model parameters of each unit related to the size of the unit based on the size information of the unit; and step 3, simulating a concrete structure loading process by using the model parameters calculated in the step 2 to obtain a concrete structure damage finite element simulation result in which the unit size effect is eliminated. The method breaks through the bottleneck that the unit size effect in concrete structure damage finite element simulation cannot be eliminated in the prior art, and has wide application prospects in the process of objectively simulating concrete structure damage evolution.

The invention discloses a Parkinson's disease prediction method based on adaptive federal learning and related equipment. The method comprises the following steps: step 1, initializing and broadcasting a global model; 2, user local training; step 3, the user uploads the local convergence speed of the local training to the server; step 4, the server adaptively calculates and adjusts the participation rate of the current round of communication, the selected users and parameters of the next round of local training of the users according to the local convergence speed, and sends calculation results to all the users; 5, updating the global model and broadcasting again; step 6, performing iterative training and model optimization; and step 7, predicting new patient data by using the global model obtained by training to realize early prediction of Parkinson's disease. According to the method, on the premise of ensuring the global model precision, the equipment with higher convergence speed and higher contribution degree is adaptively selected to participate in training, so that unnecessary communication overhead is reduced, and the model convergence efficiency is improved.

The invention discloses an optical neural network multi-architecture adaptive computing chip and a computing method thereof. The chip comprises an optical signal input unit, an input data modulation unit, a weight and bias processing unit and a photoelectric detection unit. The optical signal input unit is used for generating and outputting an optical signal; the input data modulation unit is used for modulating and loading information carried by the source data and the bias data to an optical signal output by the optical signal input unit; the weight and bias processing unit comprises a photon weight module and a photon bias module, and the photon weight module is used for carrying out optical coding on weights and realizing optical multiplication and addition calculation of input data of optical signals and corresponding weight parameters; the photon bias module is used for bias coding, summing the bias coding and an optical multiplication and addition operation result, and outputting an operation result containing bias compensation; the photoelectric detection unit is used for converting operation results into electric signals to be output. The method has high universality, and the calculation accuracy of the optical neural network is effectively improved.