72 results about "Combination algorithm" patented technology

The invention relates to the field of construction engineering, in particular to a roadbed construction slope displacement monitoring system based on cloud computing. A sensor network is used for collecting roadbed construction data in a roadbed, wavelet transform is used for denoising the data, a dynamic time warping algorithm in a cloud computing unit is used for aligning time sequence data in the initial roadbed construction data, and the weight of each sensor data is dynamically distributed based on an entropy weight method. A hybrid prediction model is established based on an LSTM long short-term memory network and FEA finite element analysis, a slope stress field result simulated by FEA is used as a physical constraint layer of the LSTM, an attention layer is embedded in the physical constraint layer, hyper-parameters of the initial hybrid prediction model are optimized by using a Cs-Ant improved cuckoo-ant colony combinatorial algorithm, and a slope stress field is obtained. And inputting the feature roadbed construction data into the target Cs-FEA-LSTM hybrid prediction model for prediction. The manual analysis cost is effectively reduced, and the level of roadbed engineering full-period monitoring is improved.

The invention relates to a thermocouple position layout optimization method based on feature point extraction, and the method comprises the following steps: heating a patch and an adhesive through a hot patching instrument, obtaining the temperature field data of an adhesive interface based on a finite element simulation method, and extracting the temperature value and coordinates of each node; analyzing the time sequence change of the temperature field data, and screening the key frame with the maximum node temperature standard deviation; performing spatial interpolation on the node temperature data of the key frame to generate a two-dimensional temperature distribution matrix; feature points are extracted from the temperature distribution matrix through a combined algorithm of extreme value layering, OPTICS density clustering, gradient analysis and adaptive spatial distribution; and optimizing the thermocouple installation position according to the feature point distribution. Through the method, the heating conditions of the bonding interface and the patch are monitored and fed back by using the accurate number of the thermocouples and setting the accurate positions of the thermocouples, and meanwhile, the problem that uneven heating cannot be monitored due to the fact that the positions of the thermocouples are improperly set is solved.

The invention discloses a distribution box room environment parameter integrated measurement method, and particularly relates to the field of multi-parameter measurement, and the method comprises the steps: firstly constructing a machine room three-dimensional model and a sensor correlation degree matrix, then building a heat balance, humidity diffusion and airflow motion model, and calibrating parameters; deploying a measurement system and calibrating through dual synchronization and cross check; establishing a sensor confidence evaluation system and an adaptive threshold based on 72-hour reference data; fusing calibration data by adopting a three-level correlation calibration mechanism; four types of measurement modes and conversion logics are designed, and measurement resources are dynamically allocated through environmental risk assessment; multi-dimensional state parameters are extracted, a comprehensive evaluation value is calculated through normalization, dynamic weighting and combinatorial algorithms, and five-level early warning response is achieved; according to the method, multiple models and an intelligent algorithm are integrated, the parameter measurement precision and the environment risk identification efficiency are improved, the response delay is reduced, the fault diagnosis accuracy is improved, and reliable technical support is provided for safe operation and maintenance of the distribution box room.

The invention discloses a zero-trust gateway single packet authentication method and system for a new energy fan control system, computing equipment, a computer storage medium, a computer program product and a chip. The authentication method comprises the steps of generating an authentication material; exchanging and deriving a mixed key; binding Hash calculation is carried out; encrypting and assembling the message; receiving and verifying by the gateway; and binding verification and registration are carried out. The invention relates to a zero-trust gateway single packet authentication method combining a post quantum cryptography algorithm and a national commercial cryptography algorithm. The method is applied to secure communication between a wind power plant and a centralized control center, an SM2, SM3 and SM4 combined algorithm in a national secret system is combined with a post-quantum key agreement algorithm CRYSTALS-Kyber512, identity authentication, encryption and integrity verification are completed through a single message, confidentiality protection and anti-quantum security guarantee under one-time interaction are achieved, and the security of the wind power plant is improved. The method is especially suitable for new energy scenes with wide fan distribution, complex links and high real-time requirements.

The invention provides a collaborative design method for geometric parameters of a sandwich structure based on frequency response, and belongs to the technical field of sandwich structure materials for ships. According to the collaborative design method, a sandwich structure basic model of a carbon fiber composite panel and a honeycomb aluminum core is established, and a basic sweep frequency excitation test of 0.1 Hz to 1000 Hz is carried out; carrying out fusion processing on the basic excitation response data and the supplementary excitation response data through a weighted vector combination algorithm to establish a dynamic response mechanism equation considering the material performance time-varying characteristic; and constructing a geometric parameter collaborative optimization model taking minimization of mass and maximization of rigidity as targets, solving a Pareto optimal solution set by adopting a multi-target genetic algorithm, finally outputting optimized panel thickness, core material height and honeycomb cell size parameters suitable for long-term service in the marine environment, and carrying out frequency response verification test. The technical problem that frequency response characteristic prediction is not accurate due to material performance degradation under the marine environment service condition is solved.

The invention provides a financial analysis method and device based on intelligent questions and answers, and the method comprises the steps: capturing user input content through a preset information tool, and obtaining a to-be-processed question; verifying the user permission and the problem range according to the to-be-processed problem; under the condition that the user permission and the question range meet verification conditions, calculating and solving the to-be-processed question through the large combinatorial algorithm model, and displaying a question answer obtained through calculation and solving; wherein the combinatorial algorithm large model is obtained through real-time financial data training. Compared with the mode that a current universal intelligent question-answering system cannot acquire a large amount of effective data in a specific field, cannot deeply answer financial questions in the specific field and cannot provide major decision support, a mainstream interaction mode is provided for inputting the questions and outputting the answers; and the large model of the combinatorial algorithm is trained by accessing real data of an enterprise, so that financial data analysis in the deep professional field is realized.

Provided is a system and a method for scheduling. The method includes training a first scheduling policy based on a first weight and training a second scheduling policy based on a second weight that is different from the first weight, calculating a first evaluation result based on the first scheduling policy, calculating a second evaluation result based on the second scheduling policy, determining, by a processing circuit, a third scheduling policy based on inputting the first evaluation result and the second evaluation result into a policy-combination algorithm, calculating a third evaluation result based on the third scheduling policy, based on the third evaluation result, determining a third weight for the third scheduling policy by inverse reinforcement learning or by Bayesian optimization, training the third scheduling policy based on the third weight, and controlling a scheduling process based on the third scheduling policy.

In some implementations, a device may receive a request for cryptographic transmission of payload data. The device may obtain the payload data for encryption. The device may encrypt the payload data, to generate encrypted payload data, using a first encryption key, wherein the first encryption key is configured such that the encrypted payload data is decryptable using a second encryption key that corresponds to the first encryption key. The device may generate tokenized data. The device may combine, using a combining algorithm of a set of combining algorithms, the encrypted payload data and the tokenized data to generate combined data, wherein the combined data is not directly decryptable using the second encryption key. The device may provide the combined data and an indicator of the combining algorithm as a response to the request.

The invention discloses an intelligent transient control method and system for a hybrid power assembly, and relates to the technical field of hybrid power engineering machinery, and the method comprises the steps: obtaining a historical working state data set of the hybrid power engineering machinery; training an improved unscented Kalman filtering algorithm by adopting a historical working state data set; the improved unscented Kalman filtering algorithm is a two-stage combined algorithm adopting a whale algorithm and a moss growth optimization algorithm, and a dispersion degree factor and a prior distribution factor with the minimum fitness function value in the unscented Kalman filtering are generated through iterative search; and on the basis of the required power and the working state of the whole machine at the current moment, a trained improved unscented Kalman filtering algorithm is adopted to predict the change of the transient output power of the power battery and the motor and the change of the transient output power of the engine so as to control the output power of the power battery, the motor and the engine. The dynamic response of the whole machine is improved, and adverse effects on an engine, a power battery and a motor are reduced.

The application discloses an automatic planning method and device for a walking route of a land controllable seismic source, and belongs to the field of physical exploration. The application introduces a TSP idea, and finally obtains a seismic source vehicle driving route which is short in path, less in barrier crossing, less in driving restriction, and in line with the driving operation habit of a driver through steps of constructing a map, setting relevant parameters and weight ratios, and solving the walking route of the land controllable seismic source by using a TNH combination algorithm. The application combines the advantages of a two-approximation algorithm, a Heuristic algorithm and an NSGA_II algorithm to propose the TNH combination algorithm for solving the walking route of the land controllable seismic source, so that the automatic planning of the walking route of the controllable seismic source is realized, the operation efficiency is accelerated, the accuracy of operation results is improved, the calculated route is more in line with the demand of field construction, and the exploration operation is further developed intelligently. The application is applied to the field of physical exploration to plan the walking route of the controllable seismic source.

The invention belongs to the technical field of wind turbine generators, and particularly provides a wind turbine generator fault early warning method and system based on an optimized deep belief network, and the method comprises the steps: obtaining SCADA data based on a data collection and monitoring control SCADA system installed in a wind field; wherein the SCADA data comprises wind field surrounding environment parameters and wind turbine generator component performance parameters; carrying out abnormal value detection on the SCADA data by utilizing a KNN-box graph combination algorithm; performing parameter optimization on the deep belief network DBN in combination with a sparrow search algorithm SSA and a particle swarm optimization algorithm PSO, and constructing an SSA-PSO-DBN-based wind turbine generator fault early warning model; based on the wind turbine generator set fault early warning model, early warning of the wind turbine generator set fault is completed. The algorithm provided by the invention has relatively high precision in judgment of the running state of the wind turbine generator, can further reduce the risk of misjudgment, and can realize early warning of the fault of the wind turbine generator at the same time.

The present application provides a method and system for building change detection based on multi-source real-scene three-dimensional models. The method includes: obtaining the triangulation topology data of two-phase real-scene three-dimensional models within the area to be detected, and performing preprocessing and standardization processing; using plane-elevation hierarchical registration technology for automatic registration to achieve coordinate unification without manual intervention; using spatial index difference calculation to identify the three-dimensional change areas of the two-phase real-scene three-dimensional models, and optimizing the three-dimensional change areas by using a multi-scale dilation-erosion combination algorithm; using three-dimensional feature dual-channel filtering for the optimized three-dimensional change areas to verify real building changes; generating four-dimensional spatio-temporal results and outputting a visualization model including elevation change values, material types, and three-dimensional reconstruction parameters. The present application overcomes the machine-readable problem of subtle changes in buildings in large-scale real-scene models, and provides a highly reliable three-dimensional quantitative analysis tool for urban illegal construction monitoring and urban safety management.

The present application relates to the technical field of communication security, and in particular to a security authentication method, system and electronic device, comprising: sending an algorithm support list, a hybrid key exchange parameter and a first random number, a first certificate request to a second terminal, so that the second terminal generates a second handshake key and a ciphertext; determining a first handshake key and verifying a target first certificate chain sent by the second terminal using the first handshake key to obtain an authentication result, the present application performs authentication based on the hybrid key exchange parameter, so that the terminal has security against quantum attacks, reduces the leakage of plaintext parameters in the initial handshake message in the whole identity authentication process, uses different encryption processing for subsequent messages, reduces the risk of identity authentication key leakage, and improves the security of identity authentication. Meanwhile, the algorithm selection list is sent in the form of a combination algorithm, which can effectively reduce the algorithm selection matching time of the second terminal and improve the identity authentication efficiency.

The present invention discloses a structural optimization design method based on a particle swarm algorithm. For super high-rise buildings, an optimization model is established, an objective function is set, and the structural layer displacement, inter-layer displacement angle, and top-floor peak composite acceleration are used as constraints to generate an initial structural parameter matrix. Combining the structural dynamic characteristics and wind tunnel test results, a frequency domain calculation method is used to obtain the wind vibration response characteristic matrix, and the equivalent static wind load matrix and acceleration response matrix are calculated. Multiple iterations are performed using a combined algorithm of the optimization criterion method and the particle swarm algorithm. By initializing and updating the relevant parameters of the particle swarm, calculating the fitness value, iteratively updating the position and velocity matrix, and recalculating the wind load and acceleration response data, the optimal solution matrix is ​​finally output based on the convergence judgment, thereby determining the final design scheme of the structure and achieving comprehensive optimization of the structural wind resistance and economic benefits.

A kind of sewer state intelligent diagnosis method and system based on machine learning, the present application relates to the field of digital data processing, in the method, build abnormal data identification model;Abnormal data identification model is obtained based on abnormal data identification result;LGB algorithm and LSTM-Attention combination algorithm are used to clean liquid level data, and the liquid level data after cleaning is obtained;Build rainfall inflow event identification model;Identify high value state;When the duration of high value state is greater than the preset duration, time series correlation analysis is carried out between high value state time period and rainfall data, and the inflow event caused by rainfall is obtained;Trend item data is obtained;Based on trend item data, inflection point detection is carried out to obtain inflection point data;Inflection point data and topological relationship data are associated and analyzed, and the pipe and channel blockage position is obtained in combination with upstream and downstream connection relationship;Diagnosis result is generated based on inflow event and pipe and channel blockage position.The present application improves the accuracy and reliability of the diagnosis of sewer state.

The invention discloses a working condition self-adaptive joint module motor control method for a quadruped robot, which comprises the following steps: S1, after a system is started, loading a preset standard parameter library of six working conditions of constant-speed walking on the flat ground, acceleration / deceleration on the flat ground, climbing, downhill, running and jumping and obstacle crossing, and calibrating a zero point of a multi-source sensor; a flat ground constant-speed walking working condition control mode is entered by default; and S2, collecting motor parameter data in real time through a multi-source working condition sensing module, carrying out fusion analysis on the collected motor parameter data by adopting a combinatorial algorithm, and carrying out working condition identification once every 5-10ms. Core parameters are collected through fusion of the multi-source sensor, a feature extraction and fuzzy neural network combined algorithm is combined, working condition recognition is completed every 5-10 ms, the recognition accuracy of six kinds of working conditions is high, the problem of working condition distinguishing fuzziness caused by dependence of a single parameter in the prior art is solved, and the working condition recognition accuracy is improved. And differential control requirements of complex scenes such as flat ground, climbing, running and jumping and the like can be accurately met.

The invention relates to a risk control rule generation method and system, a readable medium and equipment, and relates to the technical field of natural language processing. According to the scheme, a natural language processing model is adopted to execute reasoning tasks on a risk control file data set to extract structured conditions, and a risk control condition set is obtained; and executing a condition combination algorithm in the risk control condition set, matching the structured condition to generate an initial rule, and outputting the initial rule in a standard formatting manner to provide a risk control rule. According to the scheme, natural language processing and a condition combination algorithm are combined to realize automatic analysis and interpretation of risk control files, accurate extraction of risk control conditions and generation of risk control rules according with file description, so that the conversion cost of the risk control rules is reduced, the rule extraction efficiency is improved, the updating iteration period is shortened, and the risk control efficiency is improved. In addition, the problem of deviation of risk control rule conversion is overcome to a certain extent, the structured degree of the generated risk control rule is improved, subsequent analysis and interpretation are facilitated, and improvement of processing cost and error risk caused by secondary conversion is avoided.

Embodiments of the present application relate to the field of unmanned aerial vehicles, and provide a cache communication and deployment scheme generation method and device for air-ground integrated wireless networks, computer equipment and a storage medium. The method comprises: obtaining initial model parameters; constructing an unmanned aerial vehicle communication and deployment optimization model based on the initial model parameters; solving the unmanned aerial vehicle communication and deployment optimization model by using a combination algorithm of a particle swarm optimization and a Hungarian algorithm; constructing an unmanned aerial vehicle cache optimization model based on a function relationship between optimal values of the unmanned aerial vehicle communication and deployment optimization model and decision variables; solving the unmanned aerial vehicle cache optimization model by using a Monte Carlo simulation and an improved particle swarm optimization algorithm; and generating a cache communication and deployment scheme for air-ground integrated wireless networks according to a communication and deployment sub-scheme and a cache sub-scheme. The method can reduce the implementation cost of unmanned aerial vehicle-assisted wireless networks and improve the implementation effect of air-ground integrated wireless networks.

The present disclosure relates to a digital filter arrangement (DFA) for compensating group velocity dispersion (GVD) in an optical transmission system (OTS) wherein the DFA is configured to receive a sequence of samples of a digital input signal in the time domain in the form of consecutive blocks of size L. The DFA is configured to generate M discrete Fourier transforms of a current overlap block of a size N greater than the size L and of M−1 delayed versions of the current overlap block. The DFA is configured to filter the entries of the generated M discrete Fourier transforms to generate an output discrete Fourier transform with N entries, wherein the compensation filter is implemented by a delay network and a linear combination algorithm.

The invention discloses a UI scheduling optimization method and system of an end-to-end OCR architecture, and relates to the field of software test.The method comprises the steps that text recognition is conducted on a to-be-processed picture based on the OCR image recognition technology, and a plurality of keywords of the to-be-processed picture are obtained; dynamically combining the keywords by using a preset dynamic combination algorithm to obtain combined keywords; on the basis of the combined keywords, UI automatic test cases corresponding to semantics of the combined keywords are obtained; optimizing the automatic test case of the mobile terminal UI according to the case passing rate to obtain an optimized automatic test case, and performing automatic test on the mobile terminal UI according to the optimized automatic test case through a preset automatic test algorithm; according to the method, the test case can be more accurately and comprehensively designed, and the accuracy of automatic testing is guaranteed; the automatic test case is automatically tested, and then the test case is optimized according to the passing rate, so that the more comprehensive test case is realized.

The application belongs to the technical field of path planning, and discloses a robot path planning method based on combination of particle swarm, ant colony and A-Star algorithm, which comprises the following steps: generating a grid map, obtaining positions of obstacles and feasible areas in the grid map, constructing an accessible distance matrix, determining starting positions and target positions of each robot in the grid map in a robot cluster; and using the particle swarm, ant colony combination algorithm and A-Star algorithm to perform path optimization planning on the robot cluster based on the accessible distance matrix, the starting positions and the target positions of the robots in the grid map, so as to obtain an optimal path. The application combines the ant colony algorithm, the algorithm and the simulated annealing algorithm with the particle swarm algorithm, and performs robot obstacle avoidance path optimization, so that the robot can quickly find a path from the starting point to the end point while completing obstacle avoidance.

The application discloses an adversarial training method for improving robustness of a natural language processing model, and comprises the following steps: sending original text into the model for training; generating an adversarial sample of an attack model by using a combination algorithm; and finally adding the adversarial sample into original samples for adversarial training.

The invention discloses a low-temperature plasma jet flow vibration temperature diagnosis method based on machine learning, and belongs to the crossing field of plasma parameter diagnosis and machine learning technologies. The method comprises the following steps: S1, generating a theoretical spectrum through software, collecting an experimental spectrum, and accurately calibrating a temperature label for the experimental spectrum by adopting an Euclidean and SCM combined algorithm; s2, carrying out normalization processing on all spectral data, and dividing the spectral data into training, verification and test sets; s3, constructing and training two neural network models of BPNN and MLP, and learning a mapping relation between the spectrum and the temperature; step S4, optimizing the model through multiple modes such as verification set expansion, regularization and transfer learning to prevent overfitting, and using the optimized model to carry out rapid and real-time temperature diagnosis on the plasma spectrum; the method can solve the problems that the traditional manual fitting nitrogen molecule radiation spectrum diagnosis method is long in time consumption and highly depends on the experience of an operator.

This invention provides a bidding information management system and method based on Internet technology. It collects and preprocesses bidding data from participating parties; employs a combined algorithm combining the Analytic Hierarchy Process (AHP) and entropy weighting to perform multi-dimensional real-time comparison and comprehensive scoring of the preprocessed bidding data, yielding bidding results and rankings; it encrypts the bidding data and calculation results using dual encryption technology and constructs a secure transmission channel with firewall protection; it pushes the bidding results to all participants in real time through diversified notification channels; and it displays detailed results, rankings, and scoring criteria through a visual interface, enabling transparent querying of bidding information. This invention solves the problems of delayed result calculation, untimely notification, insufficient data security, and low transparency in existing bidding systems, significantly improving bidding efficiency and enhancing public trust.

The invention discloses an AIoT-based alarm management method and device, and the method comprises the steps: firstly collecting multi-dimensional basic data through an AIoT terminal, enabling a device management system and a user operation platform to be connected to obtain a device historical record and a user operation track, uploading the historical record and the user operation track to a cloud platform after preprocessing, and constructing a multi-dimensional data set; identifying valid and invalid alarm data from the data set by using a random forest and isolated forest combined algorithm; combining the data set and alarm data, analyzing historical fault, equipment aging and environment trend characteristics through a bidirectional LSTM neural network, outputting a fault risk value, and triggering a pre-alarm; then, constructing an equipment association graph by means of a graph neural network, positioning multi-equipment alarm sources, and merging alarms; and finally, the AIoT visual collaborative operation and maintenance platform dispatches an order according to a rule to track the progress, a statistical module generates a report, and alarm full-life-cycle management is realized. According to the invention, the problems of high false alarm rate, lack of fault prediction capability and the like in the prior art are solved.

The invention relates to the technical field of geodetic parameter inversion, and discloses a horizontal hierarchical geodetic parameter inversion method, system, equipment and medium, and the method comprises the steps: constructing an inversion objective function according to an apparent resistivity prediction function and an apparent resistivity measurement value which are constructed through employing a kernel function and a first-class zero-order Bessel function; and representing the kernel function by adopting an odd polynomial and an even polynomial, representing the odd polynomial and the even polynomial on the basis of an adjacent layered soil resistivity change rate set and a layered thickness sum set obtained by adopting a permutation and combination algorithm, and then performing partial derivative calculation, according to the obtained first partial derivative relational expression and the second partial derivative relational expression, a complex mirror image method is adopted to obtain a third partial derivative relational expression of the inversion target function to the soil resistivity and a fourth partial derivative relational expression of the inversion target function to the layering thickness; and carrying out horizontal layered geodetic parameter inversion by adopting a trust region algorithm to obtain an inversion result. According to the method provided by the invention, the accuracy and reliability of a horizontal layered geodetic parameter inversion result are improved.

The application discloses a continuous robot inverse kinematics combination algorithm, and an inverse kinematics solving method comprises the following steps: 1, according to the single joint arm segment parameter, establishing the robot link coordinate system and the tool coordinate system; 2, based on the constant curvature method, according to the geometric relationship, establishing the transformation between the joint rotation angle and the end posture angle; 3, according to the given end posture matrix T en d, solving the end posture angle analytical solution and the inverse kinematics analytical solution; 4, according to the obtained end posture angle analytical solution and inverse kinematics analytical solution and the established transformation between the joint rotation angle and the end posture angle, calculating the inverse kinematics approximate solution; 5, taking the obtained inverse kinematics approximate solution as the initial value, using the numerical iteration method based on the motion Jacobian matrix, calculating the inverse kinematics accurate solution. The inverse kinematics combination algorithm is clear and intuitive, has the advantages of high calculation precision and fast operation speed, and reduces the error generated by the traditional inverse kinematics solving method based on the constant curvature method.

The embodiment of the application discloses a multi-modal data-driven intelligent cabin parameter adaptation method and system, and relates to the technical field of intelligent cabin human-computer interaction. The method synchronously collects physiological data, multi-modal interaction data, driving behavior and scene data, extracts core features after preprocessing, adopts an evidence theory attention mechanism combination algorithm to construct a dynamic fusion adaptation model, realizes accurate and real-time adaptation of multiple cabin parameters such as sitting posture, vision, control and environment, and covers special groups such as the elderly, children and disabled users and all driving scenes. The application solves the defects of the prior art, such as insufficient data fusion dimension, static adaptation model, lack of psychological support and special group adaptation, parameter prediction error and whole-process delay, significantly improves driving safety and driving comfort, and is suitable for parameter optimization of various intelligent cabin products.

This invention discloses a method, system, medium, and product for constructing an elevation datum based on a low-order cosine-corrected Stokes kernel function, relating to the field of physical geodesy. The method includes: calculating model data using an Earth gravity field model, topographic data, and a high-frequency topographic influence combination algorithm; determining the direct and indirect effects of topographic compression on ground gravity anomalies and the elevation datum based on topographic compression; determining empirical and test values ​​of the correction order of the low-order cosine-corrected Stokes kernel function based on the cumulative error of the model gravity anomaly and the test frequency bandwidth; calculating an empirical and test elevation datum using the low-order cosine-corrected Stokes kernel function based on the empirical and test values ​​of the correction order; and determining the final elevation datum based on the empirical and test elevation datums. This invention can improve the accuracy of elevation datum construction.

The invention discloses an SPA data packet encryption method based on national cryptography, which comprises the following steps: an SDP client generates an SPA data packet containing authentication information, and encrypts the SPA data packet by using national cryptography algorithms SM2 and SM4; the SDP client sends the encrypted SPA data packet to an SDP controller; and after receiving the SPA data packet, the SDP controller decrypts and verifies the authentication information in the SPA data packet. According to the invention, a combined algorithm of a symmetric encryption algorithm and an asymmetric encryption algorithm is used, and the asymmetric encryption algorithm is used for key transmission, so that the security of key transmission is improved. The symmetric encryption algorithm is used for encrypting and decrypting the data, and the symmetric encryption algorithm has the advantages of small calculation amount, high encryption and decryption speed and high encryption efficiency. The advantages of the symmetric encryption algorithm and the asymmetric encryption algorithm are combined by using the combined algorithm of the symmetric encryption algorithm and the asymmetric encryption algorithm, so that the security of key transmission is improved, the encryption and decryption efficiency is improved, and the method can be applied to a large-scale network flow scene.