44 results about "Global iteration" patented technology

The invention provides a hybrid intelligent path planning method and system for enteromorpha tracking and monitoring, and relates to the technical field of marine ecological environment monitoring, and the method comprises the steps: obtaining the data of a monitoring region, generating an initial path meeting basic constraints based on a rasterization cost model through employing a Dijkstra algorithm, and then taking the path as an initial solution, and carrying out the optimal path planning through employing a Dijkstra algorithm; carrying out global iterative optimization with monitoring coverage rate, equipment energy consumption and obstacle avoidance safety as targets by adopting a snakelike optimization algorithm, and carrying out smoothing processing on an optimized path; and meanwhile, path re-planning is dynamically triggered based on real-time feedback data. According to the method, the technical problem that the initial feasibility, the global optimality and the dynamic adaptability are difficult to consider in unmanned monitoring equipment path planning in a complex marine environment is effectively solved, and the efficiency, the accuracy and the reliability of enteromorpha tracking monitoring are remarkably improved.

The invention relates to the technical field of industrial adaptive control, in particular to an automatic processing parameter optimization method and system for a five-axis numerical control machine tool. The specific implementation process comprises the following steps: carrying out discrete analysis on machine tool parameters containing a cutter shaft vector and a translation shaft position coordinate, and generating time-varying geometric features; constructing a numerical control machine tool simulation model according to the material constitutive equation and the frequency domain response function, calculating a regenerative flutter prediction sequence, and dividing a real-time parameter feasible region by combining global rigidity and local flexibility data; and performing global iteration optimization on the processing parameter function group by using a multi-target optimization algorithm in the feasible region, and locking a Pareto optimal solution set to perform automatic optimization on the processing process. According to the method, the simulation model containing the physical dynamic characteristics and the real-time parameter feasible region are constructed, flutter and errors caused by stiffness matching imbalance are avoided, collaborative optimization of multiple target variables is achieved, and the productivity efficiency of the numerical control machine tool is remarkably improved on the premise that the machining precision is guaranteed.

The invention provides a communication network deployment method, device, equipment and medium, and the method aims at a target relay node randomly determined from an initial network deployment scheme, if the migration probability of the target relay node is not smaller than a migration probability threshold value, the target relay node is migrated to obtain a first intermediate network deployment scheme, and the first intermediate network deployment scheme is a second intermediate network deployment scheme. And on the basis of the first intermediate network deployment scheme, local position optimization is performed on at least part of relay nodes in the area to which the relay nodes belong, so that a candidate network deployment scheme is obtained, and if the candidate network deployment scheme meets a preset condition, the candidate network deployment scheme is used as an initial network deployment scheme to perform global iteration, so that the initial network deployment scheme is obtained. And finally determining a target network deployment scheme to perform entity network deployment. According to the embodiment of the invention, the entity network deployment precision can be improved.

The application provides a mixed intelligent path planning method and system for Enteromorpha tracking monitoring, and relates to the technical field of marine ecological environment monitoring. The method comprises the following steps: obtaining monitoring area data, generating an initial path meeting basic constraints by using a Dijkstra algorithm based on a gridding cost model, then taking the path as an initial solution, performing global iterative optimization with a snake optimization algorithm for the purpose of monitoring coverage, device energy consumption and obstacle avoidance safety, and performing smoothing processing on the optimized path; meanwhile, path re-planning is dynamically triggered based on real-time feedback data. The application effectively solves the technical problem that path planning of unmanned monitoring equipment in complex marine environment is difficult to balance initial feasibility, global optimality and dynamic adaptability, and significantly improves the efficiency, accuracy and reliability of Enteromorpha tracking monitoring.

The application belongs to the technical field of state monitoring, and specifically relates to a robot operation state real-time monitoring method and system based on end-cloud cooperation, which comprises the following steps: collecting original data, completing noise reduction, alignment, repair and coding according to a kinematic model to form a standardized time series data set. The local model and early warning rules are used to carry out risk preliminary judgment, divide abnormal types, and schedule transmission to output encrypted data packets. The cloud analyzes data and local judgment results, combines historical working conditions and environmental information to carry out space-time fusion analysis, revises and corrects with the aid of a risk model, and outputs a comprehensive judgment report. The platform formulates an optimization scheme according to task priority and field layout, and pushes control instructions in a chain link. The terminal executes instructions and returns state data, and the end-cloud synchronously updates the model and rule library relying on incremental learning, and the cloud completes global iteration to realize system autonomous evolution. In the application, data research and risk judgment are managed through multiple links to build a robot self-evolution monitoring system.

The application relates to the technical field of tunnel engineering disaster prediction, and discloses a soft rock tunnel large deformation prediction method based on a particle swarm optimization neural network, which comprises the following steps: selecting geological construction characteristic parameters such as water content of surrounding rock and construction methods as input factors, and establishing a qualitative index and quantitative value mapping; based on orthogonal test design and numerical simulation, engineering sample data sets are constructed and normalized; subsequently, the initial connection weight and bias of an error back propagation neural network are globally iteratively optimized by using a particle swarm optimization algorithm, so that optimal initial values are obtained by taking network errors as fitness values; finally, data training is carried out to generate a mature model, and a predicted deformation value is output and combined with a double check mechanism to carry out engineering early warning. The application solves the problems that traditional neural networks are slow in convergence and prone to falling into local optimization due to random parameter initialization, and effectively improves the prediction accuracy of soft rock tunnel large deformation and the reliability of engineering early warning.

This invention provides a method for reconstructing continuous wheel-rail forces based on discrete fastener reaction forces, comprising the following steps: Step 1, establishing a wheel load distribution model; Step 2, establishing an algorithmic framework for the inversion method, by introducing a responsibility region and a local deconvolution strategy, mapping the discrete fastener reaction force FRF to the continuous wheel-rail force WRF. This invention effectively solves the problems of traditional onboard sensors' difficulty in long-term fixed-point monitoring of specific road sections, and the inability of existing ground discrete monitoring points to fully characterize the continuous wheel-rail interaction behavior. By constructing an inversion framework based on Gaussian load distribution using a "responsibility region + local deconvolution" approach, it fully utilizes the load transfer characteristics of the vehicle-track coupling system, achieving high-precision reconstruction of continuous wheel-rail forces using ground discrete fastener reaction force data. This eliminates the need for complex global iterative optimization and can accurately restore the dynamic characteristics of wheel-rail contact even with only a limited number of discrete measurement points.

The application discloses a federated learning method, device, system, storage medium and electronic equipment. The method comprises the following steps: in each global iteration process, receiving a modified gradient item in the current global iteration process; in each local iteration process in the current global iteration process, determining a local gradient item of the machine learning model in the current local iteration; correcting the local gradient item of the current local iteration based on the modified gradient item, updating the model parameter of the machine learning model based on the corrected target gradient item, and executing the next local iteration process based on the updated model parameter; and in the case that the local iteration process is completed, sending the model parameter change in the current global iteration process to the center server node. The embodiment of the application avoids the case that each computing node falls into a local optimal value in the local training process, and improves the generalization performance of the machine learning model obtained by training.

The invention belongs to the technical field of automatic driving, and particularly relates to a local-to-global iterative optimization lane line detection method and application, and the method comprises the steps: obtaining an input road image, and extracting a multi-scale feature map of the road image; global feature coding is carried out on the multi-scale feature map to output confidence, the first K query vectors are dynamically selected from a group of learnable query vectors based on the confidence, and the query vectors correspond to potential lane line instances; initializing the selected query vector into a learnable lane line anchor box; inputting the initialized lane line anchor frame and the coded multi-scale feature map into a multi-stage decoder for iterative optimization; and calculating to obtain a lane line detection result by using the lane line parameters output by the last-stage decoder. The method is high in adaptability to complex scenes, and the lane line expression normal form can reduce the training complexity while having the fine-grained fitting capability.

The present application relates to the technical field of industrial adaptive control, in particular to a machining parameter automatic optimization method and system for five-axis CNC machine tools. The specific implementation process includes: discretely analyzing machine tool parameters containing tool axis vector and translational axis position coordinates to generate time-varying geometric characteristics; constructing a CNC machine tool simulation model according to a material constitutive equation and a frequency domain response function, calculating a regenerative chatter prediction sequence, and dividing a real-time parameter feasible region in combination with global stiffness and local flexibility data; performing global iterative optimization on a machining parameter function group in the feasible region by using a multi-objective optimization algorithm to lock a Pareto optimal solution set for automatic optimization of the machining process. The present application avoids chatter and errors caused by unbalanced stiffness matching by constructing a simulation model containing physical dynamic characteristics and a real-time parameter feasible region, and simultaneously realizes the collaborative optimization of multi-objective variables, thereby significantly improving the productivity efficiency of the CNC machine tool under the premise of ensuring machining accuracy.

The application belongs to the technical field of model training, and provides a differential privacy federated learning method based on noise scale allocation and related equipment. Embodiments of the application obtain a total noise scale sequence of a target client under a total training step number, and allocate a noise scale sub-sequence to a plurality of local training steps of the target client in a current global iteration based on the total noise scale sequence; the total noise scale sequence is an increasing sequence determined by minimizing the convergence upper bound of a global model corresponding to a server under the constraint of a total privacy budget of the target client; the target client performs the current global iteration based on a decreasing learning rate sequence and a mini-batch stochastic gradient descent algorithm, and performs gradient noise processing according to the noise scale sub-sequence. Finally, the current global model is updated based on the aggregated cumulative noise gradient. Embodiments of the application can be applied to differential privacy federated learning of mini-batch local training, and can improve model performance while protecting privacy.

The invention discloses a beam forming and trajectory joint optimization method for an unmanned aerial vehicle communication and inductance integrated system, and belongs to the technical field of wireless communication. The method comprises the following steps: clustering ground users, and selecting a user cluster for service in each time slot; establishing a joint optimization problem for maximizing the average downlink communication rate of the UAV-ISAC system under the conditions of transmitting power constraint, user minimum rate constraint, unmanned aerial vehicle kinematics constraint and sensing threshold constraint; decomposing a joint optimization problem into three optimization sub-problems of user cluster scheduling, common rate allocation and beam forming and unmanned aerial vehicle trajectory; a linear programming method, a positive semi-definite relaxation and successive convex approximation method and a continuous convex approximation method are respectively adopted to solve the sub-problems of user cluster scheduling, non-common rate allocation and beam forming and unmanned aerial vehicle trajectory optimization, and finally a joint optimal solution is obtained through global iteration. The communication rate of the system can be effectively improved on the premise that the sensing threshold is met, and cooperative gain of communication and sensing is achieved.

The invention relates to the technical field of federated learning, and provides a collaborative security and incentive federated learning method, which is characterized by comprising the following steps that: a server selects an optimal client subset; each client in the optimal client subset performs performance evaluation on the global model and the local model and generates an alarm decision; calculating an optimal local training batch by each client in the optimal client subset; the clients participating in this round of training generate model updating based on the alarm decision; the server judges a benign client set based on the alarm information; aggregating the model update corresponding to the benign client set to generate a new generation of global model; broadcasting a new generation of global model to the optimal client subset for the next round of training; and repeatedly executing until the total number of rounds of global iteration is reached. According to the invention, the problem of inherent conflict and tradeoff between security and incentive in federated learning for a long time is successfully solved.

This invention discloses a method and system model for detecting user electricity theft based on consortium blockchain and federated learning. The detection method includes the following sequential steps: 1) Node registration; 2) Training initialization; 3) Key distribution; 4) Local training: Selected participants download the global model and iterate through it using a local optimization strategy, then encrypt the model and send the encrypted data to the proxy nodes; 5) Model aggregation: A cluster of proxy nodes that meet the threshold jointly recovers the decryption key and calls a smart contract to execute a two-stage aggregation, with the result uploaded to the blockchain after consensus; 6) Global model update: After global iteration meets the conditions, global training ends, and the electricity theft detection model is updated; 7) Electricity theft detection. This invention can obtain a model with performance exceeding that of local individual training while also ensuring the privacy of local data, thus improving security, and enabling continuous iteration and updating of the user electricity theft detection model.

This invention discloses a data-driven adaptive parametric modeling method for automotive rooflines, comprising the following steps: acquiring multiple sets of side-view roofline coordinate data of real vehicles, dividing the side-view roofline into a front guide segment and a rear main body segment; establishing a normalized coordinate system for each segment, and then discretizing it into several sampling points to form a bottom-level sample database; defining control variables, including the spatial position of control points in the normalized coordinate system, the number of control points, and the type of reconstructed model; employing a two-layer optimization strategy to perform global iterative optimization of the control variables for each segment to obtain the optimal control variables; constructing a comprehensive evaluation index, and selecting the control variable with the optimal comprehensive evaluation index from all parametric reconstructed models that have undergone inner-layer optimization as the optimal control variable, thus obtaining the optimal parametric reconstructed model. This invention aims to solve the problems of existing designs relying on manual experience and the difficulty in achieving an optimal balance between fitting accuracy and model complexity.

The invention relates to the field of intelligent crossing of material mechanical property testing and calculation, in particular to an intelligent global iteration recognition method for indentation impact factors based on composite indentation data. The method comprises the steps of establishing a mapping database based on a material constitutive model; updating the mapping database according to the residual indentation morphology and the maximum load in the mapping database; constructing a forward artificial neural network, and training the forward artificial neural network by using the updated mapping database; acquiring a plurality of groups of residual indentation morphology and maximum load in the indentation experiment of the to-be-tested material, and converting the residual indentation morphology and maximum load into actual standardized characteristic quantity irrelevant to the size of the indenter; and constructing a composite error function related to the residual indentation morphology and the maximum load, carrying out global optimization on the indentation influence factor, and outputting the indentation influence factor of the to-be-tested material. In this way, multi-source indentation information can be fused, friction and depth changes are fully considered, and the method has high calculation efficiency and strong generalization ability.

This application relates to the field of data processing technology and discloses a method for processing heterogeneous multi-scale data in Gaussian process regression. The method first acquires the raw data and performs feature transformation, standardization, and dimensionality reduction preprocessing to construct a multi-level validation dataset. Second, it reconstructs the Gaussian process regression kernel function, introducing a learnable Minkowski distance parameter to replace the traditional fixed Euclidean distance metric. Next, it uses a tree-structured Parzen estimator strategy to perform global iterative optimization on the validation set with the goal of minimizing prediction error, determining the optimal Minkowski distance parameter. Subsequently, it trains the model based on the optimal parameter and an automatic correlation determination mechanism, jointly optimizing hyperparameters such as signal variance and length scale through maximum likelihood estimation. Finally, it performs prediction and outputs the mean and variance. This invention, through adaptive parameterization of the kernel function metric, effectively overcomes the shortcomings of traditional models in adapting to non-spherical distributions and multi-dimensional heterogeneous features, significantly improving prediction accuracy and generalization performance.

The invention discloses an intelligent production scheduling method and system for cable flexible manufacturing, and relates to the field of production management scheduling, and the method comprises the steps: firstly, through data solidification, comprehensively capturing dynamic information such as an emergency order, a current plan, a factory state and model changing time, and forming a context basis of a decision; then, a rapid heuristic algorithm is used to carry out cost quantitative evaluation on all possible insertion schemes of the emergency order, and a high-quality initial scheduling solution is rapidly generated; and finally, introducing a simulated annealing element heuristic algorithm, and carrying out global iterative search by taking the initial solution as a starting point so as to find an optimal solution with minimum disturbance to the whole production plan. According to the method, a complex experience decision is converted into a data-driven automatic optimization process, rapid and accurate adjustment of the production plan is realized, and the overall stability and efficiency of the production line are improved.

Embodiments of the present application provide a method and device for constitutive model construction and parameter optimization of honeycomb materials, computer equipment and medium, relating to the technical field of electronic digital data processing, wherein the method comprises the following steps: obtaining original experimental data sets generated by mechanical experiments of the honeycomb material under multiple different working conditions; fitting to obtain a shape function representing the stress-strain relationship under a reference working condition, constructing a density function representing the influence of density on mechanical properties, using an exponential nonlinear function, constructing a strain rate function representing the influence of strain rate on mechanical properties, and constructing a temperature function representing the influence of temperature on mechanical properties; constructing a constitutive model of the honeycomb material based on multi-factor correction factors; taking the minimum mean square error between the stress-strain curve predicted by the constitutive model and the target curve as the optimization target, performing global iterative optimization and outputting. Through the constructed constitutive model, the problem of strong dependence of traditional model parameter fitting and poor universality is solved.

This invention relates to the fields of seismic resistance in geotechnical engineering and construction technology in hydraulic engineering, and particularly to a vibration energy dissipation feedback control system for core wall anti-liquefaction. The system includes: a data acquisition and positioning module that acquires vibration response signals and location information in real time during the compaction process; a signal processing and feature extraction module that extracts characteristic parameters representing the dynamic state of the fill material; a real-time liquefaction risk diagnosis module that outputs a quantified liquefaction risk index using a pre-trained discriminant model; an intelligent decision-making and control command generation module that generates control commands based on this index; an actuator adaptive adjustment and targeted compaction module that drives the roller to adjust its working parameters and performs targeted enhanced compaction in high-risk areas; and a global iteration and visualization monitoring module that coordinates the entire process and generates a quality risk distribution map. This invention achieves real-time perception and adaptive closed-loop control of anti-liquefaction performance during construction, fundamentally improving the seismic stability of the core wall.

The invention relates to the technical field of free surface seepage, and discloses a free surface seepage global iterative solution method and system based on an immersive boundary method. The method comprises the following steps: acquiring seepage data such as dam seepage, slope rainfall infiltration and underground water coupling; generating a global computing grid, managing grid encryption / decryption, and recording node coordinates and a topological relation; outputting a water head / pressure field; the geometric accuracy of the free surface is maintained based on the real-time updating of the free surface position of the water head field; processing the constraint of a solid boundary on seepage, and converting a boundary condition into a global unified source item; carrying out global iteration; iterative convergence is judged, and the time step length is adjusted; analyzing the free surface seepage, comparing a global iteration result with an analysis result, and if a comparison result is greater than a preset error value, performing readjustment; and if the error value is smaller than the preset error value, outputting the free surface dynamic evolution video. According to the method, the calculation cost is reduced through global iteration and self-adaptive step length optimization, and efficiency and stability are both considered.

The invention discloses an ocean network federated learning-oriented resource allocation method and system, and relates to the technical field of ocean networks, and the method comprises the steps: calculating the uploading rate and data transmission time of each underwater sensor, calculating the training time of the model training ship for local model training, the execution agent model training time of the model training ship and the training time of the model aggregation ship for agent model training in the single round of global federated learning iteration; constructing a global iteration total time delay of the ocean federated learning system model, constructing an energy consumption constraint condition of the ocean federated learning system model, and constructing a resource optimization model for minimizing federated learning training delay; and solving the resource optimization model based on an iterative search method to obtain resource optimization parameters of the ocean federated learning system model. According to the invention, the technical problem that the underwater sensor cannot directly participate in the cooperative training process in the traditional ocean federated learning environment is solved.

This invention discloses a power device packaging optimization method based on a BPNN-WOA fusion model, relating to the field of power device thermal management. It addresses the problems of traditional optimization methods, such as neglecting nonlinear coupling of parameters, difficulty in global optimization, and handling complex discrete variables. This method relies on a database of heat dissipation influencing factors and a high-fidelity 3D thermal simulation model. Parameters are selected and standardized to generate a sample dataset, which is then used to train a BPNN thermal resistance prediction surrogate model. The WOA algorithm's whale position vector is mapped to a combination of packaging parameters, and the BPNN output is mapped to a fitness value. The optimal parameter combination is obtained through global iterative optimization using WOA, and verified by simulation after inverse normalization. If the deviation exceeds a threshold, additional samples are added to retrain the model. This method can capture the synergistic effect between parameters, achieving global optimization in a continuous-discrete hybrid design space, significantly reducing packaging thermal resistance, and improving optimization efficiency and accuracy.

The application discloses a kind of based on task driving and feature joint optimization's metasurface structure design method.The method constructs the binary metasurface physical model consisting of K encoding area;Utilize deep learning network to extract K target spectrum vector group from target spectrum data set, as the optimization reference of each encoding channel;With the average value of the cosine similarity between each channel simulation response vector and corresponding target vector (task matching item) and the average value of multichannel spectrum response standard deviation (orthogonality item) constitute joint fitness function;Global iterative optimization is carried out to the 0 / 1 binary sub-pixel space distribution of each channel using genetic algorithm, to realize end-to-end task-driven reverse design.The application directly anchors the target spectrum obtained by dataset learning as the driving index of physical structure optimization for the first time, and the average standard deviation of the optimized multichannel is significantly better than that of the random structure, which can effectively improve the accuracy of the rear-end spectrum reconstruction.

The application discloses a traffic anomaly detection model training method and device, and belongs to the technical field of network communication. The method comprises the following steps: obtaining initial model parameters of current round iteration training and a candidate training node set; initializing a local to-be-trained traffic anomaly detection model based on the initial model parameters; performing local training on the initialized to-be-trained traffic anomaly detection model based on local traffic data, and obtaining updated model parameters; selecting a candidate training node which has not performed global iteration training of the current round according to a gradient distance matrix between training nodes; and sending the updated model parameters to the training node to perform local training based on the updated model parameters. The method shares the updated model parameters among multiple training nodes, performs local training based on local traffic data, iteratively updates the model parameters, trains the traffic anomaly detection model, improves the model training efficiency, and improves the convergence accuracy of the model.

The application discloses an FMD parameter self-adaptive adjustment method based on VPPSO optimization, and belongs to the technical field of bearing fault parameter self-adaptive adjustment methods.S1: obtaining a bearing vibration signal in a strong noise environment as original input information for FMD parameter optimization;S2: constructing a VPPSO double-population collaborative optimization model to determine the optimization range of the mode number n, filter length L and cutting frequency band number K of the FMD;S3: taking the reconstructed signal loss function value as the fitness index of VPPSO, and performing global iterative optimization on the key parameters of the FMD through the double-population collaborative optimization mechanism to output the optimal FMD parameter combination; through the VPPSO double-population collaborative optimization, the FMD parameter is automatically adjusted, manual trial and error are not needed, different fault types such as inner rings, outer rings and rolling bodies are adapted, and different fault diameter scenes of 0.007-0.021in are adapted, multiple fault types and diameters are adapted, the limitation of traditional one parameter and one working condition is solved; compared with the non-optimized PSO, the VPPSO reduces the convergence iteration number and greatly shortens the optimization time, and greatly improves the engineering application efficiency.

The invention relates to the technical field of power system power transmission line on-line monitoring, and discloses a power transmission line icing risk early warning method fusing induced electricity characteristics and meteorological data, and the method comprises the steps: collecting induced electricity and corridor micrometeorological multi-source heterogeneous data, and executing deviation standardization processing; constructing a full-link parameter collaborative optimization strategy, carrying out joint coding on variational mode decomposition parameters and least square support vector machine hyper-parameters, and carrying out global iterative optimization by using an improved lizard optimization algorithm and taking verification set error minimization as a target; performing signal decomposition and sample entropy extraction based on the optimal parameter combination, and constructing a multi-dimensional complexity index; and inputting the reconstructed model to carry out regression calculation, and outputting an icing early warning value through reverse normalization reduction. According to the method, through full-link parameter cooperation and multi-source feature deep fusion, adaptive matching of a signal processing layer and a prediction layer is realized, the defect of independent parameter optimization is overcome, and icing prediction precision and response speed are improved.

The application discloses a kind of transposed step-by-step optimization methods of cascade hydropower station interplant load distribution, comprising: obtaining basic hydrological engineering data and power grid load demand, generating the initial cumulative output sequence of each hydropower station;With cumulative output as state variable, in the order of first time period and then library, optimization is carried out using transposed step-by-step optimization algorithm;In the optimization process, the dynamic feasible region of the cumulative output of the current hydropower station is constructed, and the physical operation constraints of each reservoir are strictly checked;The optimal cumulative output point is selected by calculating the consumed energy storage objective function value;Based on the optimization result after global iteration convergence, the water head and load filling scheduling instructions are output.The application directly converts load balance into boundary constraint of iterative feasible region, reduces the calculation overhead of exploring infeasible solution, and improves the optimization efficiency of algorithm and cascade water utilization rate.

The application discloses a kind of underwater binocular camera calibration methods and systems of fusion polarization information, belong to marine environment monitoring technical field.The application constructs the isomerous binocular system under active polarization illumination, left eye camera is equipped with rotatable linear polarizer, right eye camera is full transparent;By collecting parallel and orthogonal polarization images, clear polarization feature image is solved using orthogonal polarization difference denoising model, left eye polarization feature point and right eye gray feature point are extracted respectively;Further construct joint error optimization objective function, water body real-time refractive index and polarization plate installation error are taken as state variable and included in objective function, and nonlinear optimization algorithm global iteration optimization is used.The application can effectively suppress water body backscattering noise, eliminate dynamic refractive index and mechanical error influence, realize the high-precision in-situ calibration of binocular camera under turbid water, and hardware change is small, engineering adaptability is strong.

The present application relates to the technical field of information security and privacy protection, and aims to replace the Gaussian noise exceptionally added in the differential privacy technology with quantization error, and realize privacy protection for the local client. The privacy protection-oriented deep quantization federated learning method trains the deep quantization network by using the parameters of the updated model obtained after the local data of the client k is trained in the t global iteration process, then quantizes the updated model by using the trained deep quantization network and changes the quantization noise distribution to obtain the quantized index value, and the index value is sent to the server side after being losslessly encoded. The server side receives and decodes to obtain the aggregation update, obtains the updated global model, and distributes it to the user for the next round of federated learning iteration. The present application is mainly applied to the privacy protection communication occasion.