94 results about "Model control" patented technology

The invention discloses a quadruped robot control method and device.The method comprises the steps that quadruped robot joint angle information is obtained, and the quadruped robot joint angle information comprises hip joint adduction and abduction angles, hip joint flexion and extension angles and knee joint bending angles; calculating a rotation error vector according to the joint angle information of the quadruped robot; according to the rotation error vector, a model prediction control objective function is constructed, and the optimization objective of the model prediction control objective function is that the error between a prediction model and an expected state is minimum; according to the model prediction control objective function, quadratic programming solving processing is carried out, and a foot end force prediction value is obtained; generating a joint torque command by using a virtual force model control method; and robot control is conducted according to the foot end force predicted value and the joint torque command. The quadruped robot control method realizes quadruped robot control, improves environmental fitness and anti-rollover capability, and reduces calculation complexity and response delay. The method can be widely applied to the technical field of robot control.

The invention relates to the technical field of industrial process control, and discloses a digital twinning-based fermentation process temperature adaptive control system, which comprises a data acquisition and state representation module, a probability generation type digital twinning module, an adaptive risk management module, an opportunity constraint model prediction control module and a model iteration trigger module, according to the method, probability distribution of future states is predicted by constructing probability generation type digital twinning, and the uncertainty of the model is quantified in real time; the system can dynamically adjust the risk tolerance, drives the chance constraint model prediction controller, seeks an optimal control solution on the premise of ensuring the process safety probability, triggers the model adaptive update by means of an online evaluation mechanism, and realizes the optimal control solution on the basis of effectively managing the process uncertainty. The fermentation temperature is subjected to high-performance self-adaptive control considering safety and economy, and the problem that a traditional deterministic model is insufficient in control robustness is solved.

A substrate treatment apparatus includes a nozzle, a moving mechanism, a storage portion, and a control portion. The learned model is generated by learning, as learning data, learning target speed information indicating a moving speed of the nozzle and the amount of treatment acquired by executing a treatment on a substrate that is a learning target while causing the nozzle to move at a speed based on the learning target speed information. The control portion causes speed information at the time of treatment to be outputted from the learned model by inputting a target amount of an amount of treatment to the learned model. The control portion controls a moving mechanism such that the nozzle moves at a speed based on the speed information at the time of treatment when the treatment is executed on a substrate that is a treatment target.

The invention relates to the technical field of non-minimum phase systems, and relates to a model control method and device based on fuzzy weighted fusion, equipment and a medium, the method comprises the following steps: obtaining an output signal, and obtaining a first error value and a dynamic error change rate; obtaining a weight coefficient output by the fuzzy system in the current iteration round; obtaining a target reference model of the current iteration round according to the weight coefficient; obtaining a feedforward controller of the current iteration round according to the weight coefficient; and dynamically adjusting the control parameters of the PID controller according to the design parameters of the target reference model and the feed-forward output signal of the feed-forward controller. According to the double-degree-of-freedom model adopted by the invention, feedforward control and feedback control can be independently designed and optimized, the adjustability and flexibility of the double-degree-of-freedom model are improved, and different control requirements can be met; and meanwhile, through cooperation of the feedforward controller and the target reference model, an output signal of the non-minimum phase system can accurately track a reference target signal.

The invention relates to the field of imitation learning in intelligent intelligence, in particular to a two-hand collaborative flexible operation method. According to the method, an innovative control framework is provided for solving the problems of high-degree-of-freedom control and data collection of two-hand collaborative flexible operation, and the framework comprises an action model and a classifier model. The motion model comprises a two-arm model, a left hand model and a right hand model, motion instructions of corresponding modules are predicted respectively, the high-degree-of-freedom control process is decomposed, and the model prediction difficulty is reduced; and the classifier model outputs the category score of each module, and calls the action model according to the classification result. Besides, for training data of the four models, a modular collection strategy is provided based on an existing remote control data collection mode, and the training data requirement can be effectively met. According to the method, the two-hand cooperative control process is decoupled, and modular control is realized by using the classifier model and the three action models, so that the model control difficulty is effectively reduced, and the two-hand cooperative task success rate is improved.

The invention relates to an energy storage electric power spot market simulation clearing method based on hierarchical model control prediction, and the method comprises the following steps: S1, building an electric power spot market global simplified clearing model containing energy storage, and taking the model as an upper prediction control model; s2, establishing an electric power spot market detailed clearing model containing energy storage as a lower execution model; s3, dividing long-time simulation into M solving periods; in each solving period, global simplification clearing optimization is firstly utilized to obtain an energy storage final state SOC constraint value of each remaining period; on the basis of the energy storage final state SOC constraint value of the current period, market simulation clearing of the current period is completed by using a detailed clearing model; and finally, updating the system state according to the current clearing result, and rolling to advance to the next cycle until clearing of all cycles is completed. According to the method, dynamic global optimization of the energy storage SOC constraint is realized through a hierarchical model prediction control mechanism, and the calculation efficiency and the market simulation clearing effect are effectively balanced.

The invention provides a mechanical arm trajectory tracking control method and system combining data driving and model control. The method comprises the following steps: firstly, establishing a dynamic linearization model (PFDL) for a mechanical arm, and designing a model-free adaptive controller (MFAC); meanwhile, based on trajectory tracking errors, a sliding mode control (SMC) method is introduced to enhance the robustness and control performance of the system. According to the method, a double-delay deterministic strategy gradient algorithm (TD3) in reinforcement learning is creatively fused, and controller parameters are adaptively adjusted through online learning, so that the controller can dynamically find an optimal parameter combination under a fixed structure, and an optimal control effect is realized; according to the method, the dependence of traditional model driving on an accurate mathematical model is broken through, the method can adapt to variable environments and complex trajectories, the control precision and the error convergence speed are remarkably improved, and a new solution is provided for trajectory tracking control of the mechanical arm.

The invention belongs to the technical field of supercritical carbon dioxide system simulation, and particularly relates to a Modelica-based supercritical carbon dioxide compressor modeling method. Comprising the following steps: decomposing the supercritical carbon dioxide compressor; determining interface attributes of data transmission between the compressor model and the outside; constructing a control body model and a connecting pipe model of the supercritical carbon dioxide compressor; the control volume model corresponds to a mass and energy control equation of the two-fluid six-equation; packaging into a supercritical carbon dioxide compressor model; and connecting the constructed supercritical carbon dioxide compressor model with a pipeline and a boundary. The method has the advantages that based on the Modelica language, only specific physical phenomenon equations of the model need to be given, specific solving principles and solving steps do not need to be given, a simulation platform where the Modelica model is located can automatically compile and solve existing equations during solving, and the modeling threshold is greatly reduced.

The present invention relates to the field of robot motion control and provides an interactive control method and system with frequency adaptability, comprising: a robot obtaining a user's position trajectory and calculating a velocity time series based on the user's position trajectory; constructing a motion model and a feedback controller based on the velocity time series; integrating the motion model and the feedback controller to obtain an integrated control model; introducing frequency adaptation into the integrated control model to obtain an integrated control model with frequency adaptability; and controlling the robot to perform follow-up motion using the integrated control model with frequency adaptability. The present invention considers how the robot can make corresponding adjustments to achieve frequency adaptation and achieve dynamic equilibrium with the participant's motion frequency under conditions of human-machine interactive motion collaboration, when the participant's motion frequency changes. Furthermore, this adaptation is long-term and stable, resulting in a better robot following effect.

This application discloses a method and apparatus for constructing a Traditional Chinese Medicine (TCM) diagnostic and treatment knowledge model. The method includes: transforming a general-purpose basic language model into a model specifically for the TCM field to obtain a basic TCM model; controlling the obtained basic TCM model to learn diagnostic and treatment thinking within the TCM field to obtain a basic TCM diagnostic and treatment model; aiming to enhance the flexibility of logical reasoning in the obtained basic TCM diagnostic and treatment model, controlling the obtained basic TCM diagnostic and treatment model to learn the diagnostic reasoning paths of clinical cases in the TCM field to obtain a TCM reasoning diagnostic and treatment model; controlling the obtained TCM reasoning diagnostic and treatment model to learn the unique TCM knowledge and unique diagnostic and treatment thinking of a target renowned physician to obtain a unique TCM diagnostic and treatment model; and controlling the obtained unique TCM diagnostic and treatment model to favor the unique diagnostic and treatment thinking of the target renowned physician to obtain a TCM diagnostic and treatment knowledge model. This improves the diagnostic accuracy and clinical decision-making reliability of the model.

The invention relates to a P13 configuration electric drive assembly efficiency test system and method, and the system comprises a rack control system, a model control system, a data collection system, a battery simulator, a first load motor, a second load motor, an engine simulation motor, and a power analyzer. The first torque rotating speed sensor, the second torque rotating speed sensor and the third torque rotating speed sensor adopt a model control system, can calculate the running state of each component according to a target accelerator signal sent by the rack control system, and enables each component to automatically run according to a calculation result; and the rack control system acquires information from the data acquisition system and performs calculation to obtain the dynamic efficiency of the P13 configuration electric drive assembly, the dynamic efficiency accords with the dynamic use working condition of a common user, and the efficiency under the use working condition of the common user can be comprehensively evaluated. The system can simulate the driving condition of a real person, the system can operate automatically, and the efficiency test of the P13 configuration electric drive assembly under any dynamic cycle condition is realized.

A model representing a physical object in a shape optimization according to a design objective, and a control point for altering a shape of the physical object are received. The shape is defined by a set of nodes in the model. Sibling models are generated from the model according to a perturbation scheme. The control point is perturbed with respective perturbed values for the sibling models. Each sibling model contains nodal location changes for the nodes. The nodal location changes are determined based on a respective shape function formulated according to a respective perturbed value at the control point and one or more simulated physical behaviors of the model. The model is updated to have an optimal value for the control point. The optimal value is identified from a relationship according to the design objective. The relationship correlates a physical characteristic of the sibling models to the respective perturbed values.

The embodiment of the invention provides a thermal management control method and device, a model training method and device, equipment and a storage medium. The method comprises the following steps that: cloud equipment constructs a multi-physical domain coupling training environment, customizes a multi-target reward function and a training model, and adapts the computing power of a vehicle controller through lightweight; the vehicle end is provided with progressive access mechanisms such as driving working condition judgment and heat management request recognition, three-level grading fault processing is matched, five-layer engineering post-processing such as amplitude limiting and rotating speed fusion is carried out on a model control signal, cooperative control over a battery and a cabin shared compressor is achieved, and finally closed-loop rolling regulation and control are formed. According to the scheme, the problems that a traditional strategy has no dynamic decision-making ability, a reinforcement learning model falls to the ground and breaks down, thermal management starting is extensive, and fault management and control are failed are solved, the battery temperature is accurately stabilized in the target interval, system energy consumption is dynamically optimized, and the vehicle end adaptability, the operation robustness and the whole vehicle safety guarantee ability of a thermal management system are greatly improved.

The invention discloses a high-order model control method based on a linearization model and GA-ADRC, and relates to the field of heat exchanger control simulation, and the method comprises the steps: constructing a single-shell-pass single-tube-pass heat exchanger model which comprises a shell and a heat exchange space, the heat exchange space is divided into a shell pass region and a tube pass region, and the shell pass region is internally provided with a plurality of baffle plates; carrying out heat balance analysis to obtain a dynamic equation of each heat exchange link of the heat exchanger, carrying out discretization processing, and carrying out linearization processing on the mass flow rate and the temperature of the shell pass hot fluid to realize decoupling; and an ESO state observer is arranged to estimate the state variable of the heat exchanger in real time, and the single-shell-side and single-tube-side heat exchanger model is controlled through a GA-ADRC-based nonlinear model control method. According to the method, the complex heat transfer problem can be linearized, a GA-ADRC controller independent of an accurate model is designed, the actual nonlinear model can be successfully adapted, the adjustment time is shortened, and the control precision and the anti-interference capability are effectively improved.

The utility model discloses a semitrailer driving simulation system, and belongs to the technical field of driving simulation training. Which comprises a cockpit, a main control circuit is arranged in the cockpit, and is characterized in that a semitrailer model is arranged, a model control circuit is arranged in the model, and a main controller circuit is in wireless connection with the simulation control circuit; the model comprises a vehicle body plate (10), a model control circuit and the vehicle body plate (10) are relatively fixed, a power unit and a steering unit are arranged at the bottom of the vehicle body plate (10), the model control circuit is connected with the steering unit, and front wheels (8) are symmetrically installed on the two sides of the steering unit. According to the semitrailer driving simulation system, by arranging the model in wireless connection with the cockpit and controlling the action of the model through the cockpit, the trainee can observe the driving state of the semitrailer at the third visual angle, and especially the training level of operation such as backing up of the semitrailer can be improved.

The application relates to a catheter control device, method and storage medium. The device comprises an acquisition module for acquiring a catheter radius, a real-time position of the catheter, a real-time respiratory amplitude of a target object and a first three-dimensional model; a determination module for determining a target object radius according to the real-time position; a first control module for controlling the catheter to run in a first branch of the target object according to a first registration matrix and the first three-dimensional model when the difference between the target object radius and the catheter radius is greater than a preset difference value; and a second control module for determining a second three-dimensional model according to the real-time respiratory amplitude, a preset respiratory amplitude, the real-time position, the first registration matrix and the first three-dimensional model, and controlling the catheter to run in a second branch of the target object when the difference between the target object radius and the catheter radius is not greater than the preset difference value. The method can improve the catheter control accuracy.

Some embodiments of the teachings herein include a motion control method. An example includes: creating a motion control model; training an online reinforcement learning model using the model; producing feedback with a controlled object, a model control value, and an initial control value; calculating a reward using the control value and the feedback; generating a residual control value using the online reinforcement learning model based on the reward, the model control value, and the feedback; controlling motion of the object with the residual control value and the model control value; sending the motion control model, the model control value, the feedback, and the reward to the cloud; training an offline model with the motion control model, the model control value, the feedback value, and the reward; and updating the existing online model using the offline model or deploying the offline model in a motion control system without an online model.

The application discloses a kind of fruit fly larva tumbling movement modeling and optimization method based on material point method.For the collaborative design challenge of larva tumbling movement model and the problem that traditional method relies on manual measurement data and training time-consuming, a neuro-mechanical model is designed combined with the biological characteristics of fruit fly larvae, and an artificial neural network based on deep learning is constructed as a model controller for training, and a hyperparameter optimization method based on TPE algorithm is designed.The above method is embedded into the differentiable programming framework, realizing the high integration and efficiency optimization of the method.The method can effectively simulate the tumbling movement behavior of fruit fly larvae and optimize it under limited computing resources.The soft body model with the biological characteristics of fruit fly larvae has the advantages of flexible movement and adaptation to complex environment.The application explores the optimal control strategy of fruit fly larvae tumbling process, simulates its interaction with complex environment, and provides an important reference for the design of fruit fly larvae-like soft robot in reality.

The invention relates to the technical field of hot-rolled strip steel production, in particular to a hot-rolled strip steel plate shape control method based on roll shape and model fusion. By optimizing part of parameters for calculating the convexity of the finish rolling inlet plate blank in the secondary control model, the control precision of the model is improved; meanwhile, by correcting the convexity range of working rolls of the F1-F2 racks, the roll shifting strategy is optimized, and the problem of medium waves in the strip steel rolling process is solved; according to the method, the roll shape of the four-time CVC working roll of the F3-F4 rack is optimized, the plate shape can be finely adjusted while the basic convexity is kept, and the problem that strip steel edge thinning control is not ideal is solved; the automatic convexity control gain coefficient is adjusted in real time, so that the self-adaptive closed-loop control of the whole process can be realized; the plate shape precision and the section quality of the hot-rolled strip steel can be effectively improved, then the production efficiency of products is greatly improved, the rejection rate, the repair rate and the spot rate of the products are reduced, the application prospect is wide, and large-scale popularization is easy.

The invention discloses a refrigerating machine room intelligent management and control system and method based on dynamic correction, and relates to the technical field of intelligent management and control. The system comprises a data acquisition module, a model management and control module, a scheme optimization module and an instruction execution module. The method comprises the steps of collecting and verifying full-link equipment operation data of a refrigerating machine room, and generating an initial adjustment scheme; deducing a linkage failure path caused by an adjustment action in the management and control model and quantifying the probability, rejecting over-limit adjustment content in combination with a dynamic failure probability threshold, and obtaining a target adjustment scheme through collaborative verification iterative optimization; and based on the failure rule correction model parameters, converting the target scheme into a time sequence regulation and control instruction to be issued and executed, and forming closed-loop management and control through execution effect verification. According to the method, the failure risk can be pre-judged and adjusted in advance, equipment regulation and control conflicts and cascading failures are avoided, the adaptive capacity of the model is improved, the operation energy efficiency and stability of the refrigerating machine room are improved, and the operation requirements of the efficient refrigerating machine room are met.

The present invention provides a simulation method for a retaining ring structure in a chemical mechanical polishing process, comprising constructing a CFD model of the chemical mechanical polishing process, dividing a fluid region into discrete grids, wherein the grids are non-repetitive control volumes, and within each control volume, using a momentum equation, a continuity equation, and a VOF model to control the motion of the polishing liquid fluid, and a DPM model to control the motion of the abrasive, integrating the momentum equation, the continuity equation, and the VOF model for each control volume to obtain a discrete equation, solving the discrete equation to obtain simulation data, the simulation data including abrasive motion data, plotting the abrasive distribution and the abrasive aggregation state on the polishing pad and / or the wafer edge based on the abrasive motion data, and having the beneficial effects of visualizing the polishing liquid / abrasive flow through a finite element method, helping to understand the influence of the retaining ring on the polishing liquid flow, providing a retaining ring structure improvement to solve wafer edge over-polishing, conforming to actual production processes, and having high simulation accuracy.

The present invention discloses the field of imitation learning in embodied intelligence, and in particular, a method for collaborative dexterous operation of both hands. This method proposes an innovative control framework, including a motion model and a classifier model, to address the high-freedom control and data collection problems of collaborative dexterous operation of both hands. The motion model includes a dual-arm model, a left-hand model, and a right-hand model, which respectively predict the motion instructions of the corresponding modules, decompose the high-freedom control process, and reduce the difficulty of model prediction; the classifier model outputs the category score of each module, and calls the motion model according to the classification result. In addition, for the training data of the four models, this paper proposes a modular collection strategy based on the existing remote control data collection method, which can effectively meet the training data requirements. The present invention decouples the two-hand collaborative control process and uses a classifier model and three motion models to achieve modular control, thereby effectively reducing the difficulty of model control and improving the success rate of two-hand collaborative tasks.

The invention provides an intelligent nursing system and method integrating personalized traditional Chinese medicine fumigation physiotherapy and active pressure protection, and belongs to the technical field of medical nursing equipment. The system comprises a treatment applicator with a non-treatment area and a treatment area, a patient state sensing module, a body pressure distribution sensing device, a posture adjusting mechanism and a control module. The control module executes two control logics in parallel: the first control logic controls the treatment intensity based on patient state data and a machine learning model to realize personalized physiotherapy; the second control logic adjusts the patient posture to achieve active pressure protection when the stress exceeds a threshold based on the body pressure data and an internal stress calculated by the biomechanical model. According to the invention, cooperative work of treatment and prevention is realized, and the comprehensive nursing quality is improved.

The invention discloses a hardware-in-the-loop test method and device based on a model order reduction control strategy, and relates to the field of model control, and the method comprises the steps: based on a vehicle modeling step, preparing model data of whole vehicle model order reduction processing for a model order reduction step; the model order reduction step comprises a data input step, a convolution operation step, a pooling operation step and a full connection layer step; based on a model order reduction step, obtaining output data after order reduction processing of the whole vehicle model; a step based on model reduced-order hyper-parameter optimization: determining an optimal hyper-parameter combination through a Bayesian probability strategy, and iteratively adjusting search; a step of verification based on the reduced-order model: carrying out data verification analysis on the output data after the reduced-order processing of the whole vehicle model, and ensuring that the error precision is within a preset precision threshold range; and based on a hardware-in-loop deployment step, deploying the order-reduced whole vehicle model in the hardware-in-loop, and carrying out comparison with calibration parameters.

The invention relates to the technical field of textile machinery control, in particular to a key quality characteristic cooperative control method for an air-jet vortex spinning machine, and aims to solve the core problems of low strength, poor evenness and high fiber falling rate of air-jet vortex spun yarns in China. The method comprises the following steps: firstly, constructing a'strength-evenness-fiber falling rate 'multi-quality characteristic coupling relation model by utilizing a cross coupling technology, measuring the coupling degree of the three parts through an entropy coupling algorithm, and clarifying an interaction rule among the characteristics; then designing a fuzzy controller based on the Lie group theory and T-S fuzzy logic, tracking tension-detail-weak ring online data and solving an optimal'balance point '; combining expert knowledge and a least square support vector machine to construct a quality characteristic analysis model and a data deviation model, and integrating through a generic relationship theory to obtain a model controller; and finally, fusing the two controllers by using an H-infinity robust control theory to form a combined controller to realize cooperative control.

The invention belongs to the technical field of ship lock model control, and particularly relates to a double-line ship lock model control system which comprises a control panel used for controlling cooperative operation between a lock chamber A and a lock chamber B, water level control and mutual filling valves. Wherein the lock chamber A and the lock chamber B are arranged in parallel; when the lock chamber A needs to drain water and the lock chamber B needs to be filled with water, the lock chamber B opens the upper lock head and the mutual filling valve at the same time, the lock chamber B can take water from the upstream and the lock chamber A at the same time, when the water levels of the lock chamber A and the lock chamber B are flush, the mutual filling valve is closed, the lock chamber B continues to take water from the upstream, the lock chamber A opens the lower lock head, and the lock chamber A continues to drain water to the downstream; on the basis of completely independent operation of a common double-line ship lock, cooperative operation, water level control and mutual filling valve control between the lock chamber A and the lock chamber B are added, so that the lock chamber A and the lock chamber B can completely independently operate, do not interfere with each other and also can cooperatively operate to save water resources.

The invention provides a spinal nerve distribution point teaching model control circuit and teaching model equipment, and the control circuit comprises at least one touch unit which is used for sensing a touch action to generate a touch signal; the control unit is used for responding to the touch signal to generate a corresponding first signal, and the first signal is at least used for recording touch position information; the signal conversion unit is used for converting the first signal into a second signal and sending the second signal to external equipment, or is used for receiving a third signal sent by the external equipment, converting the third signal into a fourth signal and then transmitting the fourth signal to the control unit; the touch unit and the signal conversion unit are both in communication connection with the control unit; by adopting the spinal nerve distribution point teaching model equipment provided by the invention, the communication function between the control unit and the external equipment is realized. The learning progress of a user can be recorded, related functions of the teaching model can be controlled through external equipment, and the defects that an existing teaching model is single in control means and cannot record the learning progress are effectively overcome.

The application discloses a kind of to the curved method of cross-flow fan blade, device, cross-flow fan and vehicle.The method comprises the following steps: the accumulation line of the blade of the fan blade on the cross-flow fan is set as a modeling control curve, and a plurality of radial curve control points on the modeling control curve are determined;For each blade, based on the accumulation line modeling requirement, the blade top phase angle of the curve control point located at the top of the blade is adjusted and the blade top is reversed, the blade root phase angle of the curve control point located at the root of the blade is adjusted based on the preset control condition, and the preset control condition is the linear relationship between the blade root phase angle and the blade top phase angle.The application realizes the accumulation line modeling of eliminating noise by setting the blade top reverse bending, and since the blade top phase angle and the blade root phase angle are only limited by the linear relationship, the number of control points required to be adjusted is greatly reduced, and the design difficulty of the accumulation line when eliminating noise is greatly reduced.

The invention relates to a method for improving the adding precision of a denitration agent and controlling the response speed of a system, and belongs to the technical field of sintering desulfurization and denitration system control methods. According to the technical scheme, system operation state parameters are obtained through a sensor system, and a data training set is prepared; a denitration agent consumption prediction model is constructed, a data training set is used for learning, and model control parameters are obtained through online operation; constructing a cascade PID (Proportion Integration Differentiation) regulator which takes the NOx concentration of a chimney outlet as a set value and takes the frequency of an ammonia water motor as an output value in the L1 system; model control parameters serve as controller feedforward and are used for L1 closed-loop control, and the running frequency of the ammonia water motor is adjusted. The method has the advantages that the control precision of denitration agent adding and the response speed of an actuator can be remarkably improved, the method is suitable for various industrial application scenes, and the control effect can be continuously optimized through a machine learning model.

This invention relates to a P13 configuration electric drive assembly efficiency testing system and method. The testing system includes a bench control system, a model control system, a data acquisition system, a battery simulator, a first load motor, a second load motor, an engine simulator, a power analyzer, a first torque-speed sensor, a second torque-speed sensor, and a third torque-speed sensor. Employing the model control system, the system calculates the operating status of each component based on the target throttle signal sent by the bench control system and automatically operates each component according to the calculation results. The bench control system then acquires information from the data acquisition system and performs calculations to obtain the dynamic efficiency of the P13 configuration electric drive assembly, which matches the dynamic operating conditions of ordinary users, allowing for a comprehensive evaluation of efficiency under ordinary user operating conditions. This invention can simulate real-person driving conditions, and the system can operate automatically, realizing efficiency testing of the P13 configuration electric drive assembly under any dynamic cyclic operating conditions.