324results about How to "Improve optimization efficiency" patented technology

The invention provides an additive manufacturing residual stress optimization method and system, and the method comprises the steps: searching a global optimal solution enabling the residual stress of an additive manufacturing part to be minimum from a technological parameter space through the combination of multi-physics field numerical simulation and an intelligent optimization algorithm; the multi-physics field numerical simulation comprises the steps of sampling from a process parameter space by using an optimization algorithm to obtain process parameters, inputting the sampled process parameters into a finite element model, performing thermal analysis on the process parameters based on the finite element model, and obtaining transient thermal distribution through thermal analysis. And performing structural analysis by using transient heat distribution to obtain residual stress under the current process parameters, and transmitting the obtained residual stress to an intelligent optimization algorithm for realizing process parameter optimization. The method has the advantages of high precision, low cost and the like, and high-precision collaborative optimization of multiple types of process parameters can be realized.

The embodiment of the invention relates to the field of large model security, and discloses a large model optimization method, a related device and a storage medium. The method comprises the steps of determining an application field of a to-be-optimized student large model; calling a preset confrontation model to generate a problem set; inputting the question set into the student large model through a preset confrontation model to obtain an output result; outputting the output result to a preset scoring model for scoring to obtain a score; calling a preset teacher model to train the student large model according to a preset high-quality data set to obtain a training result and an optimization scheme; and respectively feeding back the score, the training result and the optimization scheme to the student large model, and continuously updating the problem set to train and score the student large model until the student large model is gradually iterated to be optimal, thereby obtaining the trained student large model. According to the embodiment of the invention, the overall optimization period of the large model is remarkably shortened, the optimization efficiency of the large model is improved, and the performance of the optimized large model is further improved.

The invention discloses a method for converting a two-dimensional image of a building component into a three-dimensional model, which relates to the technical field of image processing, and comprises the following steps of: comparing the size of the three-dimensional model of the component with the size of an actual building component to obtain error data between the two, and generating corresponding accuracy through a model error data set; if the accuracy of the component three-dimensional model does not exceed the expectation, carrying out coincidence analysis on the component three-dimensional model and the building component and obtaining the coincidence degree, generating a complete coefficient according to the coincidence degree, screening out defect areas in the component three-dimensional model according to the complete coefficient, and associating to obtain the optimization priority of each defect area; and after a model optimization scheme is given for the defect areas by the building component model optimization knowledge graph, optimizing the defect areas in sequence according to optimization priorities. A corresponding model optimization scheme is provided for the defect area through the building component model optimization knowledge graph, and the optimization pertinence is higher when all the defect areas are optimized.

The invention relates to the technical field of automation, in particular to a polyolefin production parameter optimization method and device, equipment and a storage medium, and aims to improve the optimization efficiency of polyolefin production parameters. According to the method, for historical production parameter sets corresponding to a plurality of historical polyolefin categories, historical product prediction models are respectively constructed, and parameter optimization processing is iteratively executed on a to-be-optimized parameter set of a target polyolefin category through the historical product prediction models until the to-be-optimized parameter set meets a target category production condition, so that a target production parameter set is obtained. And in each iterative training, performing prediction comparison processing on the to-be-optimized parameter set through each historical product prediction model to obtain a similarity weight of each historical product prediction model, determining a recommended production parameter of the target polyolefin category according to each obtained similarity weight, updating the to-be-optimized parameter set according to the recommended production parameter, and obtaining the target polyolefin category. And executing the next parameter optimization processing.

The invention relates to a flexible operation domain evaluation and optimization method and device for a flexible power distribution network, and the method comprises the steps: carrying out the normalization quantification of a multi-dimensional complex safety margin index through analyzing a power flow linear sensitivity model, and comprehensively considering the upper and lower limit constraints of voltage, the power constraint of a line and the maximum load rate constraint of a transformer; quantitatively evaluating the flexible operation domain of the flexible interconnection power distribution network, and proposing a double-layer control and optimization model based on flexible operation domain evaluation; according to the method, the effect of mutual power complementation of the flexible interconnection device among different transformer areas when the system flexibility is insufficient is effectively exerted, the optimization efficiency can be greatly improved through linear solution on the premise of ensuring that the calculation error is small, and the system flexibility operation domain is improved.

The invention discloses a joint beam forming optimization method for an RIS auxiliary low-altitude communication system. The problem of performance bottleneck caused by interaction of active beam forming and passive beam forming is solved by adopting a genetic algorithm based on a dynamic adaptive theory. Aiming at the limitation of a standard optimization method, a hybrid strategy of genetic algorithm optimization and a full-connection neural network is explored, and intelligent collaborative setting of system parameters is realized. Simulation results show that the hybrid algorithm not only simplifies the calculation process to obtain the optimal network parameters, but also effectively optimizes the system structure by dynamically adjusting the RIS reflection configuration. On the basis, the signal transmission quality is improved, interference is reduced, and it is guaranteed that the RIS-assisted low-altitude unmanned aerial vehicle intelligent communication system stably and efficiently operates in a complex wireless transmission environment.

The invention discloses a network anomaly detection method and device based on a double-branch model, and relates to wireless communication, artificial intelligence and big data analysis. The method comprises the following steps: firstly, acquiring performance time sequence data from a communication terminal to perform data preprocessing, including oversampling, undersampling and feature selection; secondly, constructing an anomaly detection network for offline training, inputting historical data into a contrast learning branch and an attention mechanism branch, learning data structure internal association, prior association and sequence association, calculating contrast loss and reconstruction loss, and obtaining the anomaly detection network through joint optimization of a multi-task loss function; in the online detection stage, the anomaly detection network obtained through training in the offline stage is used for network anomaly detection and periodic feedback, network anomaly moment data is collected to a historical database, and anomaly detection network parameters are periodically updated.

The invention provides an optimization method for metal surface laser cladding process parameters, and relates to the technical field of metal processing optimization, and the optimization method comprises the following process steps: step 1, determining an optimization target which is mainly used for determining a performance target of a cladding process and determining a quality target of the cladding process; 2, preliminary parameter selection and test design are mainly used for determining the cladding process parameter range and constructing a test model; step 3, test implementation and data acquisition; 4, data analysis and parameter optimization; and 5, verification and feedback adjustment are carried out. An orthogonal table is constructed by setting an orthogonal test, an optimal parameter combination is obtained by utilizing range analysis, a regression model equation can also be constructed by setting a response curved surface method test, and parameters are substituted into regression model parameters for operation, so that the optimal parameter combination can be obtained, the parameter optimization efficiency can be effectively improved, and the method is suitable for large-scale popularization and application. Meanwhile, records and experiences can be formed, and the method is widely applied to production practice.

The invention provides a process optimization method for coloring an artificial fur fabric by using cations, which belongs to the technical field of artificial fur fabric coloring processes, and comprises the following steps: collecting various process parameters and quality index data required by artificial fur fabric coloring, including grey cloth specification, squeezing treatment, dye formula, dyeing process, post-treatment and the like. Establishing a coloring effect empirical model considering each process parameter, wherein the coloring effect empirical model comprises equations of dye diffusion, fixation, fiber water absorption, fluff shaping and the like; substituting the collected parameter data into the model to obtain a preliminary process parameter-effect relationship; a large number of technological parameter combinations are generated according to the parameter range, and clustering analysis is carried out by utilizing a model calculation theoretical effect. And selecting process parameters of the typical clustering center to carry out pilot test verification, and optimizing the process parameter-effect relation model. And adopting a genetic algorithm to obtain an optimal process parameter combination on the basis of the optimization model, carrying out pilot scale production verification, and finally determining an optimization process scheme.

The invention discloses an extradosed cable-stayed bridge cable force optimization method, device and system and a storage medium. The method comprises the steps that S1, cable force and bending strain energy training samples of an extradosed cable-stayed bridge are obtained; s2, optimizing SVM parameters under the cable force and bending strain energy training samples of the extradosed cable-stayed bridge by adopting an IGWO algorithm; and S3, taking the cable force value after secondary tensioning as a to-be-optimized parameter, carrying out iterative optimization by using an IGWO algorithm, taking the cable force combination output after evolution of each generation as an input vector of an SVM for nonlinear prediction, outputting a population fitness value under the current cable force, and repeatedly reducing the time cost when the optimization model carries out fitness evaluation so as to improve the fitness evaluation accuracy. And IGWO-based combination optimization is realized. By the adoption of the technical scheme, the calculation efficiency and calculation precision of the finished cable force optimization of the extradosed cable-stayed bridge are improved.

The invention relates to a network optimization method and device, computer equipment, a readable storage medium and a program product. The method comprises the steps of performing intention recognition on a task creation instruction input by a user for a network optimization task, and determining network optimization demand information of the task creation instruction; searching network optimization knowledge data matched with the network optimization demand information, and generating a network optimization plan according to the network optimization knowledge data; the network optimization plan comprises at least one network optimization measure for completing a network optimization task and operation steps in the network optimization measure; according to the network optimization plan, arranging operation tasks of a plurality of RPA intelligent agents to obtain an operation task arrangement result; and according to a task arrangement result, controlling each RPA intelligent agent to execute a corresponding operation task so as to jointly complete a network optimization task. By adopting the method, the network optimization efficiency can be improved.

The invention relates to a robot intelligent sorting path planning method and a robot intelligent sorting path planning device for stacked parts in a charging basket, an RRT * algorithm is combined with an artificial potential field algorithm, random search of the RRT * algorithm in a barrier-free free space can be reduced by utilizing an artificial potential field, and meanwhile, in a complex barrier environment close to a part pile, the robot intelligent sorting path planning method and the robot intelligent sorting path planning device have the advantages that the random search of the RRT * algorithm in the barrier-free free space can be reduced; and an RRT * algorithm is used for accurate collision-free path planning, so that the problems of low path planning precision and local minimum of an artificial potential field algorithm are avoided. On the basis, the RRT * algorithm is improved, the problems of many redundant nodes, low planning efficiency and poor path quality existing in path planning of the RRT * algorithm are solved, meanwhile, the improved RRT * algorithm is combined with the artificial potential field algorithm, the planning scale is further reduced, and random search of the RRT * algorithm in a free space is reduced. According to the method and device provided by the invention, an efficient and accurate collision-free picking path planning scheme can be provided for industrial application of mechanical arm material frame sorting.

The invention discloses a parameterization level set blade topological optimization method based on a conformal geometry theory and isogeometric analysis, and the method comprises the following steps: building an initial blade model according to an existing blade profile; performing fluid-solid coupling mechanical analysis on the blade structure model, and calculating a blade surface load; the blade structure model is mapped into a two-dimensional rectangular domain through conformal mapping, and a standard orthogonal grid with the same specification as a mapping grid is established; establishing an optimized mathematical model of the blade structure; isogeometric analysis of the blade structure; optimizing a parameterized level set; and inversely mapping the blade structure to complete the optimization process. According to the method, through the conformal mapping theory and the isogeometric level set method, three-dimensional curved surface dimension reduction processing is achieved, the calculation cost is reduced, and the optimization efficiency is improved. And meanwhile, the high-precision calculation advantage of isogeometric analysis is combined, a level set optimized smooth structure is also provided, the checkerboard problem of methods similar to variable density and the like is avoided, and the purpose of efficient and accurate optimization of the curved surface structure is achieved.

The invention discloses a reverse design method of an underwater sound absorption superstructure under a multi-material system in the technical field of vibration and noise control. The reverse design method of the underwater sound absorption superstructure under the multi-material system comprises the following steps: predicting the macroscopic sound absorption performance of the superstructure based on an equivalent medium theory; based on an energy homogenization theory of an SIMP interpolation function, establishing a superstructure topological optimization reverse design method under the multi-material system; and a proper material system is selected, and the multi-material superstructure is precisely designed by taking equivalent elastic parameters of the superstructure as targets. Compared with an existing underwater sound absorption superstructure design method, the method has the advantages that large-scale sound-solid coupling finite element calculation can be avoided, and efficient design of the superstructure and good low-frequency broadband sound absorption performance are achieved; in addition, the design method provided by the invention has universality under different multi-material systems.

The invention provides a driving danger prediction method and device based on an adaptive fuzzy neural network. The method comprises the steps that original variables used for reflecting the vehicle state are collected, dimension reduction processing is conducted on the original variables, and key variables are obtained; constructing a driving danger prediction model based on an adaptive fuzzy neural network; the genetic algorithm is utilized to optimize the preceding component parameters and the following component parameters of the driving danger prediction model, and the preceding component parameters are optimized according to the selection, crossover and mutation operation of the genetic algorithm, and the central position and the standard deviation of a membership function are optimized; the optimization of the consequent parameters comprises the step of optimizing linear combination parameters in the fuzzy inference rule; and inputting vehicle state data obtained in real time into the optimized driving danger prediction model, and outputting a driving danger value used for representing driving danger. According to the method, the model adaptability can be improved, the parameter optimization efficiency is improved, the accuracy of model prediction is improved, and accurate and real-time driving risk assessment is realized.

The invention provides a college campus planning and design multi-objective optimization method based on cGAN and NSGA-II, and the method specifically comprises the steps: obtaining a large number of existing different campus planning and design related data, and carrying out the correlation of the data with corresponding carbon emission and incremental cost data, and forming a model data set; inputting the model data set into a pre-constructed cGAN model for training, and optimizing an initial campus planning and design scheme output by the cGAN model through an NSGA-II algorithm; and performing optimization adjustment and scheme visual display on the cGAN model based on the target campus planning design. The college campus carbon emission level and incremental cost collaborative optimization model constructed by the invention provides scientific decision support for an architect to fully consider the balance between the carbon emission reduction performance and the economic benefit in a scheme stage, and is helpful to further realize a campus carbon neutralization construction plan.

The invention discloses a material management interface optimization system and method. The system comprises an optimization demand determination module and a management interface optimization module. Wherein the optimization demand determination module is used for acquiring interaction data between a target object and a material management system, and determining an interface optimization demand corresponding to the target object according to the interaction data; the management interface optimization module is used for determining a target element generation model according to the interface optimization requirement and generating an interface element corresponding to the interface optimization requirement according to the target element generation model, and the element generation model is a neural network model which is obtained through pre-training and corresponds to the element type of the interface element. According to the embodiment of the invention, the specific optimization demand is determined by analyzing the interaction data of the target object, the corresponding element generation model is determined according to the optimization demand, and the interface element is determined according to the element generation model, so that the optimization efficiency of the interface element is improved, and the personalized demand of a user on the interface element can be met.

The invention discloses an oxygen-enriched bottom blowing copper matte grade prediction method based on a GA-BP neural network, which improves optimization efficiency and model prediction precision by improving a core mechanism of a genetic algorithm. The method comprises the following steps: collecting actual production data in a stable production period of a copper smelting plant; an existing GA-BP neural network is improved by adding a self-adaptive crossover probability adjustment mechanism and a self-adaptive mutation probability adjustment mechanism in a genetic algorithm, and the self-adaptive crossover probability adjustment mechanism not only depends on individual fitness, but also is combined with global and local information to be used for enhancing global search capability and local optimization capability. The adaptive mutation probability adjustment mechanism not only combines individual fitness, but also introduces global entropy and dynamic noise influence, and meanwhile, allows the mutation probability to be increased randomly in the later stage of evolution so as to jump out of local optimum; and by utilizing the improved GA-BP neural network, taking actual production data as input, predicting the oxygen-enriched bottom blowing copper matte grade and outputting a copper matte grade value.

The invention discloses a fuel cell parameter identification method based on data driving, and the method employs an improved dung beetle optimization (IDBO) algorithm to identify parameters delta 1, delta 2, delta 3, delta 4, Rc, b and theta, obtains optimal model parameters, improves the parameter identification precision of a fuel cell model, and builds a more accurate fuel cell model. The improved dung beetle optimization algorithm is improved as follows: a Cosine-Logistic-Tent composite chaotic mapping strategy is introduced in population initialization, and the uniformity and the ergodicity of the initial position of a dung beetle population are improved; a subtraction average optimizer is adopted to update the position of the dung beetle individual rolling ball, and the development and exploration ability of the algorithm is balanced; an adaptive t distribution disturbance strategy and a greedy strategy are introduced in a dung beetle group foraging stage, so that the global search capability of the algorithm is enhanced, and the calculation time is shortened.

The invention discloses a signal timing and vehicle trajectory collaborative optimization method for an intelligent networked vehicle, and the method comprises the steps: obtaining a parameter set through employing different vehicle following models according to different types of vehicle following modes corresponding to different types of vehicles on a road; acquiring state information of vehicles in a communication range through road infrastructures, and transmitting the state information to an upper signal timing optimization layer; according to the parameter set, taking the green time ratio of each phase as a decision variable, taking the state information of the vehicle in the communication range as basic input information, and constructing an upper-layer signal timing optimization model; on the basis of the trigonometric function optimization model, a lower-layer vehicle trajectory optimization model is constructed by judging whether the vehicle meets trajectory optimization conditions or not; and according to the upper-layer signal timing optimization model and the lower-layer vehicle trajectory optimization model, circularly optimizing signal timing and a vehicle trajectory. According to the embodiment of the invention, the traffic optimization capability can be improved, the optimization efficiency is greatly improved, and the method can be widely applied to the technical field of intelligent traffic.

The invention relates to a code modification strategy determination method and device, equipment, a medium and a program product. The code modification strategy determination method comprises the steps of extracting key code features in a to-be-optimized code based on a random forest model; based on a support vector machine model, classifying the key code features to obtain a target optimization suggestion to which the to-be-optimized code belongs; and based on the retrieval enhancement generation model, according to the target optimization suggestion, adjusting the to-be-optimized code to obtain a target code. By means of the method, automatic positioning and automatic modification of the to-be-optimized points of the to-be-optimized codes are achieved, and the accuracy and optimization efficiency of the to-be-optimized codes are improved.

The embodiment of the invention provides a network loop position detection method and system, a storage medium and electronic equipment, and the method comprises the steps: responding to the starting of loopback detection operation, generating a first loopback detection message, and enabling the first loopback detection message to carry first forwarding information corresponding to an output port of source equipment; a first loopback detection message is sent to forwarding equipment in the target network, the forwarding equipment is used for indicating equipment except the source equipment in the target network, and the first loopback detection message is updated by adding second forwarding information corresponding to the forwarding equipment; receiving the updated first loopback detection message, and adding first forwarding information corresponding to the ingress port of the source equipment into the first loopback detection message to generate a second loopback detection message; and detecting the network loop position of the target network based on the second loopback detection message. The problems that a loop confirmation scheme in a network is not visual enough, and specific equipment and interface information of the network loop cannot be determined are solved.

The invention relates to the technical field of radio communication, and discloses an XL-RIS-assisted near-field multi-beam design method and system, and the method comprises the steps: constructing an XL-RIS-assisted near-field communication multi-user receiving signal model; constructing a near-field multi-beam optimization model; the near-field multi-beam optimization model is solved, so that an XL-RIS beam reflection matrix can be obtained; and iteratively solving the near-field multi-beam optimization model to obtain a reflection matrix of a base station transmitting signal to the XL-RIS, and completing XL-RIS assisted system multi-beam optimization. Compared with the existing calculation, the beam optimization method provided by the invention does not need a complicated calculation problem, overcomes the problems of low convergence speed and high calculation complexity in large-scale array processing of the traditional method, and improves the optimization efficiency. The operation speed can be increased under the condition that the system performance is guaranteed.

The invention discloses an automatic extraction method for semiconductor device model parameters, and the method comprises the steps: obtaining a multi-target response through the key parameters of a semiconductor device model, and constructing and training an agent model to predict a target response mean value and uncertainty parameters; constructing a multi-objective optimization collaborative circulation framework assisted by an agent model; generating a candidate parameter population in key parameters according to a position updating mechanism of a multi-objective optimization algorithm, predicting candidate parameters by using a trained proxy model, selecting the candidate parameters according to a prediction result to carry out real simulation evaluation, adding an obtained real sample into a non-dominated file, and updating the file based on a non-dominated relationship. Adding a training set dynamic updating agent model into the real sample, generating a potential search trajectory, selecting a representative representative Pareto approximate solution from the potential search trajectory, and updating the position of the population and the direction of the next-generation search trajectory according to the representative solution selected from the file; and terminating the circulation, outputting the solution set in the file, and checking.

The invention discloses a quadruped robot form optimization method based on Monte Carlo tree search space migration. The quadruped robot form optimization method comprises a pre-learning stage and an optimization stage. In the pre-learning stage, the whole space is divided by directly using source task data; in the optimization stage, a subspace is iteratively selected for searching, a target task is sampled and evaluated in the space, and space division is adjusted according to observation values. According to the method, an effective solution is provided for efficient form optimization of the quadruped robot by adopting the method for carrying out search space migration based on the Monte Carlo tree. On one hand, hot start is provided for the morphological parameter optimization task of the quadruped robot, so that the optimization efficiency is improved; and on the other hand, the task weight can be dynamically adjusted according to the similarity between the tasks, and the division result of the search space is automatically adjusted, so that a plurality of potential compact subspaces are provided for the morphological parameter optimization of the quadruped robot, and the optimization speed and accuracy can be improved.

The invention discloses a continuous wind tunnel pressure measurement test data monitoring and correcting method, and relates to the technical field of wind tunnel pressure measurement tests, and the method comprises the steps: constraining a corresponding sampling frequency for each sensor in a sensor group; acquiring wind tunnel operation data by a sensor group, constructing a corresponding abnormal coefficient according to the operation data, and judging whether the current operation of the wind tunnel is abnormal or not according to the abnormal coefficient; performing a simulation test on a to-be-tested sample by using the wind tunnel flow field digital twinborn model, constructing the reliability of the wind tunnel flow field digital twinborn model according to test data, and if the reliability is insufficient, analyzing to obtain a corresponding difference point; and giving a corresponding optimization scheme by the model optimization knowledge graph, optimizing the wind tunnel flow field digital twin model, and performing simulation test on a to-be-tested sample by the optimized wind tunnel flow field digital twin model. The reliability of the digital twinborn model of the wind tunnel flow field is improved, the output simulation test data is more fit with the actual test data, and the reliability of the simulation test is improved.

The invention provides a Bayesian-based parameter optimization method and system for quantum key distribution, relates to the technical field of quantum information processing, and aims to solve the problems that in the prior art, the calculation amount is large, the efficiency is low, and real-time parameter optimization of a high-speed quantum key distribution system and the optimal configuration requirement of large quantum communication network resources are difficult to realize. The method comprises the following steps: acquiring system parameters, a sending probability in an MDI-QKD protocol, and intensities of a signal state and a decoy state; the obtained data is preprocessed; according to the preprocessed data, a parameter optimal model based on Bayesian optimization is constructed, training and testing are carried out, and a trained model is obtained; and inputting to-be-predicted system parameters and the sending probability, signal state and decoy state intensity data in the MDI-QKD protocol into the trained model, and outputting optimal parameters. According to the method, the to-be-optimized system parameters can be accurately predicted, the system parameter optimization efficiency is improved, the time cost is reduced, and the key generation rate is greatly improved.

The invention discloses an artillery bore opening flow field proxy model construction method based on a physical information neural network, and belongs to the technical field of test evaluation. According to the artillery bore opening flow field proxy model construction method based on the physical information neural network, N-S solution loss and empirical formula loss in a loss function are combined through the hard constraint thought, and the loss function is simplified. The method firstly describes a basic framework of the method, secondly introduces a specific formula of an improved loss function, and finally verifies the superiority of the method through experiments. According to the method, the shock wave field reconstruction has obvious advantages when the data volume is low, response values of all design points in the space can be quickly obtained, and the optimization efficiency is greatly improved.

The invention discloses a patrol aircraft flight simulation verification method and system based on a digital model, and belongs to the technical field of aircraft flight simulation. The system comprises a launch control device digital model, a scene simulation digital model, a seeker model, an autopilot model, an aerodynamic model, a steering engine model, a simulation solution model and an inertial navigation model, and digitizes the environment, a target, a seeker, a steering engine, inertial navigation, a power device, an autopilot and other modules in the flight process of the cruising aircraft. The operation hardware platform of each module is virtualized, on the premise that the original system communication mode is not changed, guidance and control of the flight process of the cruising aircraft and evaluation of performance such as miss distance can be completed, and the influence of various environment changes and structure changes on the flight of the cruising aircraft can be quickly verified through flight simulation of the cruising aircraft.

The invention provides a heat pump control system identification method based on an improved SABO algorithm, and belongs to the technical field of industrial control process system identification. The technical problem that an SABO algorithm is prone to falling into a local optimal solution in the system identification process under the condition of insufficient sample data and the system parameter identification process is solved, and the technical scheme includes the following steps that firstly, a single-input-single-output Hammerstein model of the air source heat pump control system with the dead zone characteristic is constructed, a Hammerstein system identification model of the air source heat pump control system with dead zone characteristics under the condition of insufficient sample data is obtained; and 2) constructing an identification process of the improved SABO algorithm. The air source heat pump control system parameter identification method has the advantages that the convergence speed is high, the identification precision is high, the output parameter estimation error is small, and meanwhile it is indicated that the identification method has good applicability to air source heat pump control system parameter identification.