95 results about "Optimal control algorithm" patented technology

The invention discloses a space tethered capturing system based non-cooperative target quality identification method. After a space tethered robot system captures a space non-cooperative target, a target kinetic parameter is identified in the later capture stage and a recovery stage and then self-adaptive recovery control is completed based on the identification result. The space tethered capturing system based non-cooperative target quality identification method includes firstly, providing a parameter identification method to identify quality of the space non-cooperative target primarily in the later capture stage; secondly, providing an optimal control algorithm according to influences of different recovery control algorithms to parameter identification; and thirdly, completing self-adaptive control in space non-cooperative target recovery process on the basis of the primary parameter identification result in the later capture stage.

The invention provides a rope system releasing stable control method and an electric force rope system off-tracking stable control method. The rope system releasing stable control method comprises the following steps: (1) based on a non-linear optimal control theory, determining a control variable and a state variable aiming at different processes, namely a rope system releasing process or an electric force auxiliary off-tracking process; establishing an open ring optimal control model; considering task restriction and solving an open ring optimal control rule through a pseudo-spectrum optimization algorithm; (2) designing a robust feedback controller based on a trace tracking concept by taking a solved open ring optimal trace as a reference trace, wherein a feedback control grain is commonly determined by an open ring optimal control algorithm and a numerical value interpolation. According to results of the step (1) and the step (2), a control method of a rope system releasing process and the electric force auxiliary off-tracking process is determined.

The invention relates to a lithium ion battery remaining useful life (RUL) prediction method based on a fusion algorithm and belongs to the technical field of battery management. The method comprisesthe following steps: S1, acquiring battery capacity attenuation data, and determining parameters of a RUL prediction model based on an optimal control algorithm; S2, fitting data of a training set, iteratively outputting an optimal control algorithm mode parameter filtering estimation value and a battery capacity attenuation data filtering estimation value, and obtaining an initial RUL predicted value by virtue of the model parameter filtering estimated value; S3, based on a difference value between the filtering estimation value of the optimal control algorithm and experimental data, buildingan original error sequence, taking the original error sequence as an input of a neutral network algorithm, performing continuous iteration training on the error sequence, and outputting a predictionresult of the error sequence; and S4, after the data of the training set is used, obtaining a final lithium ion battery RUL prediction result by synthesizing an initial predicted value of the optimalcontrol algorithm and the error sequence prediction result obtained by virtue of the neutral network algorithm.

The invention discloses a mixed flow assembly production scheduling control method based on a bee society self-organization model. The method comprises the following steps of: 1, setting algorithm parameters according to scale of mixed flow assembly production scheduling problems; 2, selecting a random +NEH method for an initialization function aiming at characteristics of the mixed flow assembly production scheduling problems; 3, dynamically adjusting a searching scale in neighboring areas; 4, performing an employment bee algorithm on each employment bee, and triggering a scout bee algorithm to replace the employment bee when the employment bee meets a limit parameter; 5, performing an observation bee algorithm on each observation bee, and triggering the scout bee algorithm to replace the observation bee when the observation bee meets the limit parameter; 6, performing an optimal control algorithm on the employment bee population and observation bee population; and 7, stopping the operation when iteration conditions are met, otherwise repeating the steps from 3 to 7. The method has high speed, high neighboring searching precision, high solution efficiency and good control effect, and local optimum is effectively avoided.

A method for determining a chest pressure profile for cardiopulmonary resuscitation (CPR) includes the steps of representing a hemodynamic circulation model based on a plurality of difference equations for a patient, applying an optimal control (OC) algorithm to the circulation model, and determining a chest pressure profile. The chest pressure profile defines a timing pattern of externally applied pressure to a chest of the patient to maximize blood flow through the patient. A CPR device includes a chest compressor, a controller communicably connected to the chest compressor, and a computer communicably connected to the controller. The computer determines the chest pressure profile by applying an OC algorithm to a hemodynamic circulation model based on the plurality of difference equations.

The invention relates to an active control method and system for milling chattering time delay of a motorized spindle. The method and the system are applied to the motorized spindle comprising an actuating device. The method comprises the steps: inputting vibrating signals acquired from mutually-orthogonal directions of a motorized spindle system, measured motorized spindle system parameters and a cutting force coefficient to a continuous-milling chattering active control state equation containing state time delay; carrying out discretization on the continuous-milling chattering active control state equation, then, calculating optimal active control force for the motorized spindle system by using a discrete optimal control algorithm, and then obtaining a driving voltage which drives a piezoelectric actuator to apply the active control force to the motorized spindle system. By adopting the method or the system provided by the invention, the chattering during milling can be effectively inhibited, a stable region of milling is enlarged, the machining effect is improved, and the machining efficiency is increased.

The invention discloses a grid-connected control system and method for miniature internal combustion engine generator sets. The system comprises a grid-connected controller and a rotation speed regulator ASR, wherein the grid-connected controller is used for carrying out processing of a grid-connected synchronous control algorithm, carrying out processing of an optimum control algorithm after gridconnection, sending a regulation signal obtained via the processing to a voltage regulator AVR and the rotation speed regulator ASR, detecting and comparing whether frequencies, voltage values and voltage phases of a generator set and a mains supply are the same tor not, controlling a grid-connected switch and sending a grid-connected operation signal to the rotation speed regulator ASR; and therotation speed regulator ASR is used for carrying out processing of a traditional rotation speed PID control algorithm on the regulation signal obtained by the grid-connected controller via the processing and the acquired generator rotation speed before grid connection, carrying out processing of a variable gain control algorithm after the grid connection, and using the result obtained via the processing to control a throttle percentage. The system and method are capable of solving the problem that no controllers are designed for switching between grid connection and grid separation in the prior art, and solving the defect that the electric power waves are easy to fluctuate after grid connection.

The invention relates to a self-adaptive stable equilibrium control method, a self-adaptive stable equilibrium control system and a biped humanoid robot capable of being equilibrated adaptively and stably. The self-adaptive stable equilibrium control method includes the following steps: (1) establishing a state space model of the biped humanoid robot, =A*X+B*U, wherein Y=C*X, X is a state functionof the biped humanoid robot, Y is an output function, U is an input function, A is a state matrix, B is a coefficient matrix of the input function, and C is a coefficient matrix of the output function; (2) determining an optimal control algorithm, wherein the state model of the biped humanoid robot can be converted into =(A-BK)X, the selection of the controller parameter K shall allow the characteristic value of the matrix (A-BK) to meet the set conditions, and state convergence and stable equilibrium of the biped humanoid robot can be realized. The biped humanoid robot is equilibrated adaptively by the self-adaptive stable equilibrium control system according to the self-adaptive stable equilibrium control method. The biped humanoid robot comprising the self-adaptive stable equilibrium control system can achieve stable equilibrium by itself.

The invention discloses a concurrent transmission method of a uniform interface test of a power consumption information collection system. The method comprises the following steps: using a sum of a to-be-sent data transmission request amount and a transmission data size in a last period as a to-be-transmitted data request amount in the current period; calculating a load estimation value according to the to-be-transmitted data request amount and the maximum transmission amount of a transmission path; separately calculating an available bandwidth and a congestion degree according to the feedback information of the data request of the transmission data size in the current period; adjusting the to-be-sent data transmission request amount in the next period adaptively and dynamically according to the available bandwidth, the congestion degree and the load estimation value depending on an adjustment strategy; and performing optimization and adjustment on the segmentation threshold of a load factor based on a dynamic plan. The concurrent transmission method has the beneficial effects that the dynamic adjustment of key segmentation threshold of the data transmission request amount based on the optimum control algorithm, and the high efficiency of concurrent processing of test data is ensured.

The invention discloses a catalyst feeding optimal control system and method for a continuous stirred tank reactor. The system comprises a continuous stirred tank reactor, a reactant concentration sensor, a catalyst concentration sensor, an analog to digital converter, a field bus network, a distributed control system, a main control room status display module, a digital-to-analog converter, a catalyst control valve, a coolant control valve end, and a temperature sensor. The reactant concentration sensor and the catalyst concentration sensor acquire the concentration of the material componentsin the reactor in real time. The DCS obtains an optimal control strategy of the catalyst input amount and the coolant input amount in the specified production time range by performing the internal control variable parameterization optimal control algorithm, and converts the optimal control strategy to an opening control command of the catalyst control valve and the coolant control valve to allowthe catalyst control valve and the coolant control valve to perform corresponding actions. The catalyst feeding optimal control system and method minimizes the catalyst and coolant use amount while minimizing the catalyst concentration, the reactant concentration, and the temperature deviation.

The invention discloses an automatic hole punching piling machine control system aiming at complicated terrains, and belongs to the field of building engineering. The automatic hole punching piling machine control system consists of a power distribution cabinet (1), a control cabinet (2), an air compressor (3), a windlass (4), a brake air cylinder (5), a brake operation rod (6), a clutch air cylinder (7), a clutch operation rod (8), an air pipe (9), a height sensor (10), a clamp hammer sensor (11), a pile frame (12) and a pile hammer (13), and is characterized by also comprising a FFT (fast Fourier transform algorithm) conversion system, an excessive rope optimum control algorithm system and an excessive rope expert control system. The automatic hole punching piling machine control system has the advantages that the flexible response to various geological conditions can be realized, in addition, the reliability and the stability of the system are improved, meanwhile, the hammer lifting signal collecting speed is also accelerated, and the operation time of a microcontroller is reduced.

The invention relates to an autonomous balance riding bicycle mechanical power system and a multi-rigid-body dynamic model thereof, the mechanical power system comprises a mechanical system and an electrical control system, and the mechanical system comprises a bicycle body, a rear wheel, a front fork and a front wheel; the electric control system comprises an industrial personal computer, a direct-current power supply and an electronic gyroscope, a front fork tail adjusting device is arranged at the joint of a front fork and the vehicle body, a servo driving motor is arranged on a rear wheel,and a direction adjusting servo driving motor is arranged on the front wheel and connected with the industrial personal computer through a bus; a Kane method is adopted to establish a multi-rigid-body dynamic model of the autonomous balance riding bicycle. The method has the beneficial effects that a reliable theoretical basis is provided for the structure, parameter optimization and control system design of the autonomous balance riding bicycle; an autonomous balance running bicycle state space model is established by means of a multi-rigid-body dynamics model, and autonomous balance runningof the bicycle is achieved based on a cascade stage optimal control algorithm of a speed function.

The invention provides a load balancing method. The method comprises the following steps: step one, acquiring a current value and a voltage value at each of a power source end and a load end; step two, computing the corresponding active factor, inactive factor, power factor and load rate according to the current value and the voltage value; step three, controlling a static var compensator to perform the current compensation according to the power factor; step four, computing the optimal output power through an optimal control algorithm according to the active factor, the inactive factor, the power factor and the load rate; step five, controlling a voltage regulator to output the optimal output power; step six, repeating the steps one to five. The invention further provides a load balancing device and an electricity driver. Through the adoption of the method provided by the invention, the input power of a motor can be dynamically adjusted, the capacity of a transformer is reduced, the required input power of the motor can be automatically matched, the circuit loss is reduced, and the high power factor and the obvious energy-saving effect are realized.

The invention discloses a method for programming flight paths with introduced intention of pilots. The method includes initializing setting of environmental standard parameters; acquiring data; representing maps; detecting data of constraint conditions, and to be more specific, detecting variable numbers required by mixed-integer linear programming according to objective functions and constraint data; programming the flight paths by the aid of algorithms for autonomously programming the flight paths on the basis of the constraint conditions if the variable numbers are smaller than or equal to 500; programming the flight paths by the aid of algorithms for programming the flight paths on the basis of the constraint conditions and the intention of the pilots if the variable numbers are larger than 500. The method has the advantages that the intention of the pilots is converted into restrictive conditions in the optimal control algorithms, accordingly, ideas of the pilots can be embodied by the algorithms for autonomously programming the flight paths, and the problem that intention of pilots is ignored in the traditional method for programming flight paths can be solved; the optimal control programs can be solved by the aid of approaches for combining receding horizon with the mixed-integer programming, and accordingly the problem of excessive increase of computational complexity due to addition of intention of the pilots can be solved.

The invention discloses a semiactive control algorithm for a building structure under earthquake action by a frictional damper. The steps are as follows: firstly, a structural model is selected; secondly, the structure is calculated by the linear quadrics (LQR) classic and optimal control algorithm, and the optimal controlling force during the minimum energy of the building structure is designed;thirdly, the optimal controlling force is utilized for respectively designing the real-time variable damping and the variable stiffness of the frictional damper, and therefore the semiactive frictional controlling force of the frictional damper can be obtained by the calculation and the semiactive control to the building structure is realized; and fourthly, the semiactive controlling force of thefrictional damper is utilized for controlling the building structure, and the vibratory response which is returned by the building structure under the earthquake action is collected. Indicated by theresult of the numerical experiment, the algorithm of the invention is much more effective in controlling the dynamic response of a multi-story building structure, and solves the problem of poor effectof the prior art on controlling the acceleration response of the building structure; and with the increase of storeys, the advantage on acceleration control of the building structure under the earthquake action is much more obvious.

The invention discloses an improved PID optimal control algorithm based on an RBFNN and a BAS. Firstly, aiming at a real-time control system with undefined parameters, an RBFNN model is designed, andonline system parameter identification is carried out; secondly, according to the control requirement of a real-time system, an RBF-PID model based on a local error is designed; rough setting is carried out on PID parameters to obtain a PID parameter range suitable for the system, a BAS-PID model based on ITAE optimization indexes is designed on the basis, and optimal adjustment is carried out onthe PID parameters by using the BAS-PID model to obtain optimal PID parameters. The real-time system with unknown parameters is controlled through the method, and the control effect is better than that of a self-adaptive RBFNN method, a traditional PID method and the like.

The invention provides a hypersonic gliding aircraft linear pseudo-spectrum reentry guidance method and system. The method comprises the steps of: decoupling longitudinal plane motion and transverse plane motion of a target aircraft, and establishing a longitudinal plane dynamic model of the target aircraft; based on a state equation satisfied by the state quantity of a differential flat system, establishing a target linear state equation set which simultaneously satisfies an optimal solution under an optimal control algorithm and a terminal constraint of the differential flat system; solving the target linear state equation set based on a pseudo-spectral discrete method to obtain a control quantity of longitudinal plane motion of the target aircraft; determining a course angle error control quantity of the target aircraft on a transverse motion plane based on a course angle error corridor, and constructing a heeling angle overturning logic based on the course angle error control quantity; and based on the control quantity of the longitudinal plane motion and roll angle overturning logic, guiding the target aircraft. The hypersonic gliding aircraft linear pseudo-spectrum reentry guidance method and system mitigate the technical problems of poor adaptability and low efficiency in the prior art.

The invention relates to an NNs-MRAS-based speed sensorless doubly-fed induction generator LQR control method. A differential algorithm is adopted to design a neural network (NNs) model, the neural network model is trained by a deviation backpropagation algorithm, and thus, a good rotation speed observation ability is achieved. A controller based on a linear quadratic (LQR) optimal control algorithm for stator current under a two-phase synchronous rotating reference frame is designed, the gains are controlled through state feedback, optimal control on the current closed loop parameters is realized, and the dynamic and static performance of the system is improved.

The invention provides a motor health management method and system based on full life cycle monitoring, and the method comprises the steps: firstly collecting motor multi-source sensor data, and mapping the motor multi-source sensor data into a coordinate point in a low-dimensional health state space through a manifold learning model, so as to represent the comprehensive health state of the motor multi-source sensor data; and on the basis of the expert strategy database, a health cost function associated with the health coordinates is constructed by adopting inverse reinforcement learning and is used for quantifying health loss of different operations. And a decision model is constructed, and the model is trained through an optimal control algorithm, so that an operation strategy capable of minimizing the total health cost of the whole life cycle of the motor can be found out. In actual operation, the system determines current health coordinates in real time, generates an optimal operation strategy by using a decision model in combination with future task requirements, and analyzes the optimal operation strategy into specific control parameters to be issued and executed. According to the method, active health decline path management and closed-loop control of the motor can be realized, so that dynamic optimal balance between the performance and the service life of the motor is realized.

The invention relates to a control algorithm for a pitch of a controllable pitch propeller and a rotating speed of a host machine in a stable navigation state of a ship. The method comprises the following steps that: (1), a thrust P is obtained by a formula; (2), a ship efficiency is obtained by a formula; (3), a current output power W of the ship is obtained by a formula; (4), a control criterion J is obtained by a formula according to the thrust P, the ship efficiency and the current output power W of the ship; (5), a rotating speed n is equally divided into 500 parts and a pitch H is equally divided into 100 parts; and a two-dimensional array is established; when a relationship between W [i] [j] and W(external) satisfies that the W [i] [j] is less than and equal to the W(external) and J [i] [j] reaches a maximum value, the rotating speed n and the pitch H are controlled target values. Beneficial effects of the optimized control algorithm are as follows: a rotating speed n and a pitch H are obtained though an algorithm, so that an optimized control objective including the maximum host output power, the highest fuel efficiency and the best ship maneuvering property is realized.

The invention provides a networked control device and method of a lamination high-frequency vibration fine screen. The device comprises a high-frequency vibration fine screen, an execution module connected with the high-frequency vibration fine screen, a sensor module connected with the high-frequency vibration fine screen, and a control module connected with the sensor module; the execution module is connected with the control module; the high-frequency vibration fine screen is controlled through an embedded ARM microcontroller in the control module; and the embedded ARM microcontroller is lower in system cost and more flexible in design, and can realize the newest and most optimal control algorithm. Through the structure of a networked control system, the control module is separated fromthe vibration fine screen, and the connection is realized through sharing of a communication network to leave from bad scene environment of the vibration fine screen, so that the influence on the controller by disturbance can be prevented, the centralized control of enterprise staff is convenient, the control device is prominently improved, and the enterprise benefit is obviously improved.

The invention discloses a path planning method for an overwater unmanned ship, and relates to the field of path planning. The water unmanned ship path planning method comprises the following steps: constructing a mathematical model of the movement of the water unmanned ship; analyzing an optimization target of the water unmanned ship in path planning; according to the established mathematical model, obtaining dynamics and kinematics models of the unmanned ship, and describing a ship motion law; researching the autonomous obstacle avoidance strategy of the water unmanned ship in the local area; performing optimal control research on global path planning of the water unmanned ship, wherein the global path planning refers to path planning made after all surrounding related environments are known, and the global planning needs to grasp all environment information in a motion space and arrange path planning according to the information; and researching a plurality of global path planning methods, seeking an optimal control algorithm, and finally performing simulation verification on the obstacle avoidance strategy and the path planning algorithm.

The invention provides a missile control strategy determination method and device, a storage medium and electronic equipment, and the determination method comprises the steps: determining the control quantity data of a to-be-analyzed missile system based on an initial control strategy set by the to-be-analyzed missile system; performing iterative solution based on the control quantity data to obtain a first control strategy, and obtaining a reference value function and a reference control strategy based on the first control strategy; updating the first control strategy by using the reference control strategy until the updated control strategy meets a preset control strategy updating condition, and obtaining an optimal control strategy and an optimal value function; and determining an unknown parameter matrix in the to-be-analyzed missile system based on the optimal control strategy, and determining a control strategy for the to-be-analyzed missile system in combination with a parameter equation of the to-be-analyzed missile system. In this way, unknown parameters are determined by combining the optimal control algorithm and utilizing the missile state data, and optimal control over missile trajectory tracking is achieved.

The invention provides a structured feedback risk constraint LQR solving method based on optimal control optimization, and belongs to the technical field of control optimization, and the method comprises the steps: obtaining an initial feedback gain matrix meeting a microgrid communication topology sparsity constraint; a zero-order strategy gradient method is adopted, through an outer layer iteration optimization step and an inner layer sampling estimation sub-step, a gradient direction is estimated by utilizing observation change of a target function value; according to the distributed frequency control method, strategy updating is carried out on the basis of an optimal control algorithm, a sparse feedback gain matrix obtained after N rounds of iteration is output, the sparse feedback gain matrix is directly deployed to a local controller of the micro-grid, and distributed frequency control meeting the risk constraint is achieved. Compared with a classical stochastic gradient descent method, the OCP algorithm obtains a satisfactory optimality gap, the convergence speed is higher, and the stability is also improved through dual redescription of maximum and minimum problems.

The invention discloses a multi-ship-borne isomer ship surface collaborative trajectory planning and control method based on isomorphism-tracking, and belongs to the technical field of ship surface collaborative planning and control. The method comprises the following steps: firstly, simplifying a rod traction aircraft system into a virtual aircraft system, simplifying a heterogeneous system into an isomorphic system formed by a virtual / taxiing aircraft system, and establishing an optimal control model for multi-isomorphic collaborative trajectory planning; then, solving the model by adopting an optimal control algorithm to obtain standard optimal tracks and control quantities of each virtual aircraft system and each taxiing aircraft system; and finally, enabling each rod traction aircraft system to track the virtual aircraft system standard optimal trajectory constructed according to the rod traction aircraft system, establishing an optimal control model for trajectory tracking of the rod traction aircraft system, and adopting an optimal control algorithm to solve and obtain the trajectory and the control quantity of each rod traction aircraft system. According to the method, the problem of multi-ship-borne isomer collaborative trajectory planning and control can be effectively solved, and the calculation efficiency and robustness are improved.

The invention discloses a biogas energy comprehensive utilization efficiency optimization system and method based on the Internet of Things, and belongs to the technical field of industrial automation control and energy management, the system realizes intelligent management and optimal scheduling of the whole life cycle of biogas energy through a distributed multi-level calculation control architecture, and the core is a multi-agent cooperative control platform. Comprising a fermentation agent, a purification agent, a storage agent and a utilization agent, all the agents can independently execute local control tasks and can cooperatively work to achieve global optimization, an Internet of Things sensor is deployed in the system to collect operation state data, real-time data transmission is achieved through the industrial Ethernet, and the intelligent control system is established. The marginal intelligent control node carries out local preprocessing and real-time decision making, a self-adaptive control algorithm library is innovatively integrated, the optimal control algorithm is dynamically selected according to different process links, the biogas yield and quality are remarkably improved, and practical application shows that the biogas yield is improved by 15%-25%, and energy consumption is reduced by 10%-18%.

The invention provides a navigation processing method, device and equipment, and a storage medium. The method comprises the steps of obtaining a preset route track point set; based on the preset routetrack point set, generating a navigation guide line by adopting an optimal control algorithm based on fault-tolerant optimization, the optimal control algorithm based on fault-tolerant optimization being used for carrying out fault-tolerant processing on abnormal route track points in the preset route track point set; and performing navigation processing on the vehicle based on the navigation guide line. When the navigation guide line is generated, fault-tolerant processing is carried out on the abnormal route track points in the preset route track point set, so that the problems that the navigation guide line fails to be generated due to the abnormal route track points and potential safety hazards are brought to vehicle navigation are solved, and the stability and safety of vehicle navigation are improved.

The invention discloses a permanent magnet synchronous motor weak magnetic area efficiency optimal control current tracksearch method and an online control method, belongs to the field of motors, andaims to solve the problems that a traditional weak magnetic area efficiency optimal control algorithm uses fixed parameter values for calculation, the current trackdeviation is large, and accurate weak magnetic area efficiency optimal control cannot be realized. The method is realized as follows: under a given torque instruction, a rotating speed instruction, a voltage limit and a current limit, obtaining a current working point with the minimum current amplitude as a current track with the optimal efficiency control in a weak magnetic region; the method comprises weak magnetic current angle iteration and current amplitude iteration, wherein the weak magnetic current angle iteration is carried out firstly, and the iteration direction is the direction in which the current amplitude is reduced under the voltage limit; and in the iteration process, current amplitude iteration is nested to determine the current amplitude corresponding to each current angle, the iteration direction is the direction in which the error between the given torque and the actual torque is reduced, and the current track with the optimal efficiency control in the weak magnetic region is output after iteration convergence.

The invention provides an unmanned driving anti-swing positioning method and system based on self-adaptive neural fuzzy control. The method is based on a multi-layer forward neural network and a first-order Sugeno fuzzy model, a fuzzy inference system is established through adaptive modeling, and the neural network technology is used for learning a large number of known data to achieve rapid anti-swing and accurate positioning of unmanned driving. The fuzzy inference system is established by utilizing the self-learning capability of the neural network, the membership function is adjusted and the fuzzy rule is automatically generated according to a data set generated by the optimal control algorithm, and problems of randomness of determination of the membership function of the fuzzy systemand difficulty in extraction of the fuzzy rule are solved. Under the condition that the rope length changes within a small range, the unmanned driving anti-swing positioning method and system based onself-adaptive neural fuzzy control have the advantages of being good in robustness, simple in algorithm, high in anti-swing positioning precision and the like.

The invention discloses a high-precision quantum sensing environment physical quantity real-time monitoring system and method, and belongs to the technical field of environment monitoring. The system comprises a quantum sensing front end, a signal excitation and regulation module, a quantum classical hybrid computing engine, an edge cloud collaborative transmission module and a terminal application module. The quantum sensing front end adopts a quantum entanglement sensing array integrated by NV color center diamond and a rubidium atom gas chamber to synchronously sense a magnetic field, temperature, pressure and vibration; the signal excitation and regulation module optimizes regulation parameters and inhibits noise through a quantum optimal control algorithm; a quantum-classical hybrid calculation engine is combined with a quantum neural network and classical multi-algorithm collaboration to realize efficient resolving and data fusion of quantum state features. According to the invention, the monitoring precision, the real-time response speed and the anti-interference capability are obviously improved, synchronous monitoring of multiple physical quantities is supported, and the system is suitable for various scenes such as industry, scientific research and deep space exploration, and has wide application value.