297 results about "Performance model" patented technology

The invention provides a GPU heterogeneous computing resource allocation method and system based on task scheduling, and the method comprises the steps: carrying out the feature analysis of a to-be-scheduled task, so as to extract the calculation intensity, video memory demands, inter-task data dependence and historical execution data of the to-be-scheduled task, and obtaining a task feature vector; constructing a performance model of heterogeneous GPU resources to predict execution efficiency and resource consumption of the GPU under the target task based on hardware performance parameters and running states of the GPU; and matching the to-be-scheduled task with the GPU resources according to the task feature vector and the performance model of the heterogeneous GPU resources. Through task feature analysis and GPU resource performance modeling, the adaptive relation between tasks and GPU resources can be deeply mined, to-be-scheduled tasks are accurately allocated to the most appropriate GPU, the resource utilization rate is greatly increased, the task execution sequence and resource allocation are dynamically planned, and the task execution time is effectively shortened.

The invention relates to a network slice optimization method for a 5G private network, and the technical scheme comprises the steps: constructing a time sequence performance model in a slice operation process, and monitoring tiny changes including end-to-end time delay, throughput and packet loss rate; then extracting a short-period fluctuation mode of the index, and comparing the gradient with a historical stability threshold value; judging whether the index is in a performance critical state or not by identifying an index drifting behavior which continuously jitters but does not default yet; non-equal-ratio responses including jitter improvement and delay decline after each time of resource adjustment are recorded; then trend fitting is carried out on the data, and whether a performance platform period or a critical jump point exists or not is judged; delimiting three intervals of resource investment in the model: an inactive area with invariable investment, a high-sensitivity area with rapid performance change and a rising saturation area with stable performance and continuous resources, wherein the three intervals include an inactive area with invariable investment, a high-sensitivity area with rapid performance change and a rising saturation area with stable performance; and identifying the current response area of the slice through the model, determining that the regulation strategy does not take equal proportion input as the core, and taking the optimal response area as a resource allocation interval to make a choice.

The invention discloses a wind power generation system operation optimization method based on intelligent control, and the method comprises the following steps: S1, collecting the real-time data during the operation of a wind power generation system, including the wind speed, the wind direction, the rotating speed of a generator, the output power, and the environment temperature; s2, constructing a performance model of the wind power generation system according to the real-time data; s3, based on the performance model, adjusting the operation parameters of the generator in real time to optimize the power generation efficiency of the system; and S4, when detecting that the system is abnormal, automatically triggering a fault diagnosis module, generating a fault processing suggestion and executing an optimization strategy. Through real-time data acquisition, performance model construction, operation parameter dynamic adjustment and intelligent fault diagnosis, the problems that a traditional wind power generation system is low in efficiency and not timely in fault response are solved, and the system has the advantages of dynamically optimizing power generation efficiency, timely processing system abnormity and improving operation stability.

The invention discloses a big data driven dynamic resource scheduling method for an IPv6 edge computing node, and relates to the technical field of edge computing, and the scheduling method comprises the following specific steps: S100, resource data collection and integration: deploying monitoring equipment at the IPv6 edge computing node, collecting computing, network, storage and energy resource data, and transmitting the data to a server; through multi-dimensional resource data acquisition and storage, comprehensive monitoring and quantitative integration of calculation, network, storage and energy resource data are realized, the resource scheduling is more accurate and efficient through the comprehensive data acquisition mode, and through construction of the energy consumption performance model, the performance value can be accurately calculated, and the energy consumption performance is improved. And the model is trained and optimized by using big data, so that the balance of energy consumption and performance in the resource scheduling process is realized, the resource utilization efficiency is improved, the energy consumption cost is reduced, and powerful support is provided for green and sustainable development of edge computing nodes.

The invention discloses a data disaster recovery method supporting multi-backup path switching, which comprises the following steps: generating a dynamic granularity data fragment based on the sensitivity of input data and the characteristics of a storage medium, and generating a metadata label containing a data fragment identifier, storage preference and recovery priority; constructing a heterogeneous transmission path performance model through metadata labels, generating a path quality map, distributing an optimal transmission path for each data fragment, and monitoring path performance; detecting anomalies through the standardized deviation of accumulated transmission delay and migrating fragments; carrying out parallel verification by adopting incremental integrity verification and Merkle proof stored in the block chain, and preferentially retransmitting fragments which fail in verification; according to the invention, through the cooperation of dynamic data fragmentation, backup path optimization and recovery verification, the reliability and stability of data disaster recovery are ensured so as to meet the actual business requirements.

The invention provides a helicopter flight parameter prediction method, system and device and a storage medium, and belongs to the field of helicopter performance prediction.The method comprises the steps that a helicopter and engine integrated performance model set is established; acquiring historical data including flight parameters of a helicopter, operation parameters of an engine and environmental parameters under different flight conditions and states; fitting undetermined coefficients of the helicopter and engine integrated performance model group by combining historical data with specific design data of a helicopter and an engine to obtain model coefficients conforming to specific helicopter and engine models; representing a to-be-predicted flight mission as a flight mission profile containing input parameters, substituting the input parameters of the flight mission profile into the helicopter and engine integrated performance model group with the determined coefficients, and solving the model to obtain predicted flight parameters. According to the method, reliable prediction of flight parameters of the helicopter in different flight environments can be realized, and a basis is provided for engine design, modification and use optimization.

The invention relates to the technical field of bridge engineering detection and repair, in particular to a bridge crack repair effect prediction method and system based on performance simulation, and the method comprises the following steps: obtaining basic data and environmental load data of a target bridge, and matching at least two candidate repair schemes from a preset repair scheme library; constructing an initial performance model before repair, and calculating an initial bearing capacity loss rate and a crack propagation rate; quantifying the interaction influence of the multi-modal data through a coupling factor matrix, and iteratively calculating the repair condition of a set time period after repair to obtain an initial simulation result; and collecting real-time monitoring data in a set time period in the repairing process and after repairing, and inputting the real-time monitoring data into a preset LSTM adaptive correction model to obtain a corrected simulation result. According to the method, the long-term repair condition after repair can be accurately calculated, the accuracy of a prediction result and the environmental adaptability are remarkably improved, and a scientific basis is provided for long-term service performance pre-judgment.

Techniques are disclosed in which a computer system receives, from a plurality of user computing devices, a plurality of device-trained models and obfuscated sets of user data stored at the plurality of user computing devices, where the device-trained models are trained at respective ones of the plurality of user computing devices using respective sets of user data prior to obfuscation. In some embodiments, the server computer system determines similarity scores for the plurality of device-trained models, wherein the similarity scores are determined based on a performance of the device-trained models. In some embodiments, the server computer system identifies, based on the similarity scores, at least one of the plurality of device-trained models as a low-performance model. In some embodiments, the server computer system transmits, to the user computing device corresponding to the low-performance model, an updated model.

The invention discloses a heat exchange station circulating pump characteristic adaptive optimization control method and system, and relates to the technical field of circulating pump control, and the method comprises the steps: building a building terminal heat transfer model and a building energy balance model based on the temperature distribution of the surface of a radiator, and then building a building load prediction model; performing numerical modeling on the performance curve of the heat exchange station circulating pump, and establishing a circulating pump performance model; based on a depth deterministic strategy gradient algorithm, establishing a neural network control model for self-adaptive control of the circulating pump of the heat exchange station; establishing a state, an action and a reward function of an agent of the neural network control model; and training the neural network control model to obtain a trained control model, and performing real-time control on the heat exchange station circulating pump based on the trained control model. The technical problems that a traditional control method of the circulating pump is low in operation efficiency and serious in energy waste are solved.

The invention discloses a normalization measurement method for a heterogeneous computing resource multi-dimensional index system. The method comprises the following steps: constructing the multi-dimensional index system including performance, energy efficiency and utilization rate; constructing a performance model HCRN based on the multi-dimensional index system; collecting multi-dimensional index system data in real time through a hardware monitoring tool and a performance counter, and inputting the data into a performance model HCRN after data preprocessing; constructing a reference task library to evaluate the performance of the system performance under different field applications; dynamically adjusting the weight of the performance model HCRN by adopting a reinforcement learning algorithm to realize the self-adaption of the model to task load and hardware state change; and performing performance evaluation verification and iterative optimization on the adjusted model. According to the method, a scientific and quantitative decision basis is provided for resource scheduling, model selection optimization and service deployment, the utilization efficiency of heterogeneous computing power resources is improved, the overall performance of the system is improved, and it is ensured that the system can operate efficiently and stably in different environments.

The invention relates to the technical field of elevator component performance analysis, in particular to a construction method and system for an elevator component performance model and a storage medium. The method comprises the following steps that elevator use records are collected, flow frequency spectrum modeling is carried out to generate start-stop frequency spectrum features and elevator use mode features, then part response analysis is carried out on the use mode features to obtain dynamic characteristic indexes, the independent performance of parts is calculated, and the independent performance of the parts is calculated. And an elevator interaction network is reconstructed according to the independence performance and the use mode characteristics, energy consumption distribution of the elevator is calculated, overall parameters of the elevator are obtained, framework simulation is conducted, framework movement performance loss is calculated, part position projection is conducted on a simulation framework based on part independence performance, interaction performance is evaluated, and therefore an elevator part performance model is constructed. According to the elevator part performance model construction method, elevator start-stop frequency spectrum characteristics and energy consumption distribution can be accurately described, and the response characteristics and interaction performance of the parts can be dynamically evaluated.

The invention provides a section-operation-power system joint optimization design method for an electric vertical take-off and landing aircraft. The method comprises the following steps: defining a power system design variable, a section design variable and an operation optimization variable of the electric vertical take-off and landing aircraft; a power system quality model, an aircraft profile demand analysis model, a variable pitch propeller operation performance model and a motor operation performance model are established; power system design and operation control plan making of the electric vertical take-off and landing aircraft are combined, section-operation-power system joint optimization design of the electric vertical take-off and landing aircraft is achieved, and the characteristic of great difference between the wide-speed-range operation characteristic of a variable-pitch propeller and the hovering and cruising flight stage of the vertical take-off and landing aircraft is considered to the maximum extent. And compared with the design of firstly carrying out physical system design and then carrying out operation control design, the optimal design of the overall performance of the aircraft is realized.

The invention discloses a local reasoning acceleration system and method for a large-scale MoE model under limited resources, and the method comprises the steps: carrying out the MoE model performance modeling on large language model reasoning equipment, and obtaining a module calculation performance model; executing a sliding window type greedy prediction-scheduling strategy: performing expert module routing prediction by utilizing a gating network in the MoE model to obtain activated experts of each layer and an input token of each expert, and dynamically determining the number of layers of the MoE model contained in a single scheduling window by utilizing the module calculation performance model in combination with a minimum sliding window algorithm; and according to the predicted expert module route, performing optimal execution strategy search and MoE model layer scheduling on the activated expert module in the window with the single scheduling window length. According to the method, efficient and stable local large model reasoning deployment is realized through an expert activation prediction mechanism without training cost.

The invention discloses a composite fault diagnosis method for motor overload and pressure abnormity of a wind power water cooling system. The method comprises the following steps: S1, data acquisition; the collected original signal data is preprocessed; s2, constructing a lightweight system performance model, and calculating a pressure residual error; s3, extracting time domain and frequency domain features from the original signal data and the residual signals to form feature vectors; s4, performing fault diagnosis based on a machine learning model, and inputting the feature vector into a trained model; the model outputs a diagnosis result and gives a fault confidence coefficient; and S5, outputting a result, and performing early warning. Predictive maintenance is achieved, early warning can be given out at the early stage of a fault when shutdown or secondary damage is not caused, a maintenance plan can be made in advance, passive first-aid repair is changed into active planning, the non-planned shutdown time is shortened, and the availability of a wind field is improved; the unnecessary on-site inspection frequency is reduced, the excessive dependence on the qualification operation and maintenance experts is reduced, and the troubleshooting time is shortened.

Herein, a “perception statistical performance model” (PSPM) for modeling a perception slice of a runtime stack for an autonomous vehicle or other robotic system may be used e.g. for safety / performance testing. A PSPM is configured to: receive a computed perception ground truth t; determine from the perception ground truth t, based on a set of learned parameters, a probabilistic perception uncertainty distribution of the form p(e|t), p(e|t,c), in which p(e|t,c) denotes the probability of the perception slice computing a particular perception output e given the computed perception ground truth t and the one or more confounders c, and the probabilistic perception uncertainty distribution is defined over a range of possible perception outputs, the parameters learned from a set of actual perception outputs generated using the perception slice to be modeled, wherein each confounder is a variable of the PSPM whose value characterized a physical condition on which p(e|t,c) depends.

The invention relates to the technical field of visual optimization, in particular to an offshore oil engineering underwater production system digital prototype construction and simulation data combination visual optimization design method. The method comprises the following steps: acquiring system offshore petroleum engineering project information through an interface, and creating a new project in a digital prototype platform according to the acquired project information; importing a three-dimensional geometric model of an offshore oil engineering underwater production system, and processing the three-dimensional geometric model; mounting a physical performance model on the processed geometric model, and correcting the quality factor in the mounting process; and performing CAE simulation on the three-dimensional geometric model, and importing and displaying a CAE simulation calculation result through a CAE lightweight rendering module. According to the design of the invention, a new project can be quickly created in a digital prototype platform, and comprehensive analysis and optimization of the system are carried out by utilizing a three-dimensional geometric model, a physical performance model and a CAE (Computer Aided Engineering) simulation technology.

The invention relates to a hybrid vertical take-off and landing aircraft energy management method based on a model predictive control algorithm, which belongs to the technical field of aircraft energy management, and comprises the following steps: establishing a size model and a performance model of a multi-rotor configuration based on a top demand, a hybrid architecture and a propulsion configuration by using an iterative scheme of size design optimization; forming constraint of size iteration, and optimizing required overall size parameters of the aircraft through an fmincon function; a component-level turboshaft engine model and a battery internal resistance model are provided, a theoretical model is established based on gas-heat dynamic characteristics of each component of the turboshaft engine, the battery internal resistance model is established based on battery testing, and a battery peak current is obtained; and an energy management strategy based on an MPC algorithm, DP optimization information, supplementary CS and a charge strategy.

The invention discloses an Internet of Vehicles low-delay federated learning method based on semi-asynchronous communication, and the method comprises the steps: constructing a vehicle-road cloud cooperative federated learning framework and a system performance model, building a federated learning training time delay optimization model, minimizing the overall training time delay, and meeting the vehicle energy consumption upper limit and global model precision constraint conditions; a multi-dimensional priority dynamic vehicle selection mechanism is realized, and probability sampling is carried out by using a roulette selection algorithm; a vehicle-RSU federated learning strategy based on semi-asynchronous communication and knowledge distillation optimizes the problem of lagging behind and the problem of model obsolessness; a cloud model aggregation opportunity is optimized based on a model difference metric and an adaptive cloud aggregation strategy of a dynamic threshold trigger mechanism. According to the method, collaborative optimization is carried out on multiple levels of equipment selection, intra-layer communication aggregation, old model processing, inter-layer aggregation triggering and the like, so that the overall training time delay of the Internet of Vehicles federated learning system can be effectively reduced on the premise of satisfying model precision and equipment energy consumption constraints.

The invention relates to the technical field of forklift safety analysis, in particular to a forklift safety analysis system fusing multi-modal data, which comprises a multi-modal data acquisition module, an environment-level safety analysis module, a vehicle-level safety analysis module, a behavior-level safety analysis module, a dynamic decision engine, a safety intervention module and a model optimization module. By collecting multi-modal data, constructing a dynamic environment risk model, a forklift comprehensive performance model and a sub-scene safety evaluation model and fusing multi-dimensional analysis results to generate a multi-level instruction, early warning and active control are achieved, and robustness is improved through self-optimization. The problems that a traditional technology depends on single data, a fixed threshold value fails, and closed-loop intervention is lacked are solved, and the forklift operation safety and efficiency can be comprehensively improved.

The invention provides a hypersonic flight vehicle control method based on adaptive dynamic programming, and relates to the technical field of hypersonic flight vehicle control. The method specifically comprises the following steps: building an actual performance model of the hypersonic flight vehicle, and simplifying the actual performance model of the hypersonic flight vehicle; converting the simplified actual performance model of the hypersonic aircraft into a hypersonic aircraft model in a strict feedback form; designing a state feedback controller according to the thought of a backstepping method and a feedback linearization method, and then carrying out optimization compensation on the state feedback controller by utilizing a self-adaptive dynamic programming algorithm, namely, dynamically updating network weights on line by designing weight updating laws of an execution network and an evaluation network, so as to obtain a mathematical control surface angle control quantity; and converting the obtained mathematical control surface angle control quantity into an actual physical control surface angle control quantity, and inputting the actual physical control surface angle control quantity into an actual hypersonic aircraft model to realize a state tracking control closed loop of the whole hypersonic aircraft.

Medical devices and related systems and methods are provided. A method of controlling medication delivery based on sensor input involves obtaining a measurement parameter representing an electrical response of a first instance of a sensing element to a physiological condition of a person. The measurement parameter is converted into a calibrated measurement parameter using calibration data specific to the first instance of the sensing element. The method further involves determining a measurement value using the calibrated measurement parameter as input to a performance model. The performance model is derived from historical calibrated measurement parameters and corresponding reference values. The historical calibrated measurement parameters are from other instances of the sensing element. A command is then determined based on the measurement value and sent to a medical device. The command causes the medical device to deliver a dose of medication influencing the physiological condition of the person.

Disclosed herein is a system for controlling a chilled water plant comprising one or more chillers, one or more water pumps, and a controller. The system comprise one or more processors configured to receive information comprising historical data for a plurality of system variables for the chilled water plant; determine historical performance information for the chilled water plant; prepare performance models comprising: at least one chiller predictive model; and at least one pump predictive model; prepare a field load predictive model for a field load demand; prepare a chilled water plant model; simulate operation of the chilled water plant and determining first optimised control parameters for the chilled water plant; determine an energy consumption of the chilled water plant; determine optimised control parameters for the chilled water plant; and output the optimised control parameters to the controller of the chilled water plant to optimise control of the chilled water plant.

The invention provides a performance prediction method and device based on a refrigerating machine, electronic equipment and a storage medium, and relates to the technical field of refrigeration, and the method comprises the steps that the condensation saturation temperature, the evaporation saturation temperature and the compression exhaust temperature of a refrigerating system are collected in the running test operation process, so that the saturated liquid phase enthalpy and the saturated gas phase enthalpy are determined; according to the condensation saturation pressure calculated according to the condensation saturation temperature and the compression exhaust temperature, the overheat gas specific enthalpy is determined, system refrigeration energy efficiency prediction is conducted according to the saturated liquid specific enthalpy, the saturated gas specific enthalpy and the overheat gas specific enthalpy, and the target system refrigeration energy efficiency is obtained; and performing unit performance prediction on the target refrigerator by using the target refrigeration performance model, the target system refrigeration energy efficiency, the frequency converter efficiency, the motor efficiency and the bearing transmission efficiency. According to the method, the actual refrigerating performance calculation of the refrigerating machine unit from the refrigerating cycle theory can be realized, the problem of performance prediction deviation of the refrigerating machine is effectively solved, and the performance prediction effect of the refrigerating machine is improved.

The invention provides an HPC application program communication time prediction method and system based on machine learning, and the method comprises the steps: obtaining MPI functions in an HPC application program, and classifying the MPI functions according to a communication mode; selecting an optimal machine learning fusion model according to the category of the MPI function; extracting communication features of the MPI function, and inputting the communication features to a trained optimal machine learning fusion model to obtain predicted communication time of the MPI function; and summing the predicted communication time of all MPI functions to obtain the predicted communication time of the HPC application program. According to the method, the performance model of the parallel application program is established by utilizing the comprehensive performance data, and the communication performance model is established by adopting a machine learning technology, so that the time consumption of MPI communication in the parallel application program can be accurately predicted. The method has good practicability and universality, can be universally suitable for high-performance computing application, and provides effective support for optimizing resource allocation and improving system efficiency.

The invention provides a multi-source heterogeneous data integrated management method based on a digital main line, a medium and a system, and belongs to the technical field of industrial digital main lines. A digital main line platform is used for collecting data of various formats and conducting standardization processing, a deep ontology fusion model is adopted for achieving cross-domain semantic understanding, and the data of various formats is obtained. Semantic understanding efficiency is improved through adaptive pooling feature dimensionality reduction and separable attention calculation, a storage space is optimized by adopting similarity aggregation de-duplication or a distributed independent storage strategy according to a data overlap ratio, and a digital principal line data consanguinity tracing relation is established to realize full-life-cycle data link construction. Based on the semantic mapping deviation value, a fine fine tuning or coarse tuning mode is adopted to optimize semantic understanding parameters, longitudinal integrated mapping from a function model to a performance model and a physical model and a transverse integrated framework of multi-domain comprehensive simulation are constructed, and the technical problem that cross-domain model semantic understanding accuracy is insufficient is solved.

The invention relates to a privacy enhancement federated learning method based on a conditional generative adversarial network, which is used for improving the performance of federated learning and enhancing the privacy protection capability. By designing an auxiliary data generation mechanism based on the CGAN, the training data set of the client is expanded, and the problem of data overfitting is solved. Meanwhile, a double-model coexistence concept of a private model and a public model of the client is provided, and an improved deep mutual learning model interaction training method is designed to ensure that sensitive information is only stored locally. In addition, through a high-performance model aggregation method based on prediction distribution, the performance of the global model is effectively improved. Experimental results show that the method can effectively resist various inference attacks while keeping the performance of the federal learning model, and the privacy protection capability of user data is remarkably enhanced.

The invention discloses a cluster resource allocation and scheduling joint optimization method based on multi-load expansion performance modeling, which combines resource allocation and job scheduling to optimize the energy efficiency of a high-performance cluster, prevents the cluster from falling into local optimum due to single optimization, and improves the multi-load expansion efficiency of an optimization technology at the same time. And a resource allocation stage: establishing a performance model to determine the optimal resource demand of the job. In order to reduce performance modeling overhead in a multi-type application environment, a performance peak model is established based on historical optimization information to predict an optimal resource configuration range of a new job so as to reduce a data sampling space. In the job scheduling stage, a backfill scheduling algorithm based on service awareness is provided, and when idle resources of a cluster are insufficient, the algorithm selects jobs based on a performance model to carry out resource attenuation so as to allow triggering of backfill operation. According to the method, the resource requirements of various applications in the supercomputing environment can be adaptively identified, and the resource utilization rate of the server is improved in combination with a scheduling algorithm, so that the overall energy efficiency of the cluster is improved.

The invention discloses a power quality disturbance identification method based on a Markov transition field and a lightweight dense connection network, and the method comprises the steps: constructing a PQDs standard signal set, converting a complex PQDs one-dimensional signal set into a two-dimensional feature image data set based on an MTF visual conversion method, and dividing the two-dimensional feature image data set into a training set, a verification set and a test set; the method comprises the following steps: developing a DenseNet-L lightweight identification network model based on a dense connection mechanism; a CBAM attention mechanism model combining CAM and SAM is constructed; the DenseNet-L model and the CBAM model are combined through module fusion, and a DenseNet-LC lightweight identification model is constructed and obtained; the DenseNet-LC model is trained through the training set, and an optimal performance model is stored; and performing identification performance test on the DenseNet-LC optimal performance model by using the test set to obtain a final complex PQDs identification result. According to the method, the classification precision and noise immunity of the complex PQDs can be effectively improved, meanwhile, real-time performance is achieved in the efficiency performance aspect, and technical support can be provided for rapid sensing of the operation health situation of a power distribution network system.

The invention relates to the field of playing music generation, in particular to a playing music generation method, computer equipment and a storage medium, and the method comprises the steps that a playing model pool comprises a reference model and a style model, and the style model comprises a core model and an exploration model; inputting the music score to be played into the reference model to generate basic music data; respectively inputting the basic music data into the plurality of style models to generate a plurality of pieces of target music data; generating a feedback score of each style model according to the plurality of pieces of target music data; counting a comprehensive feedback score of each core model based on a first iteration period; counting a comprehensive feedback score of each exploration model based on a second iteration period; and when the comprehensive feedback score of any style model is continuously lower than a preset score threshold, removing the corresponding style model from the playing model pool, and importing a replacement style model. Therefore, the quality of the output music is stabilized, and the style diversification and innovation of the model pool are adaptively maintained for a long time.

The invention provides a frequency determination method and device, electronic equipment, a medium and a chip, and relates to the technical field of frequency determination, and the method comprises the steps: determining the performance parameter of each module in a plurality of modules; based on the performance parameters of each module, determining a performance model and a power consumption model of each module; based on the performance model and the power consumption model of each module, determining an energy efficiency optimization model of the plurality of modules; the method comprises the steps of determining a target frequency set of multiple modules based on an energy efficiency optimization model, determining a performance model and a power consumption model of each module based on performance parameters of each module, and obtaining energy efficiency models of the multiple modules according to the performance model and the power consumption model of each model. And finally, target frequencies of the multiple modules can be obtained based on the energy efficiency models of the multiple modules, and joint frequency modulation among the multiple modules is realized.