661 results about "Setpoint" patented technology

A control system to regulate frequency of an islanded microgrid that includes at least one grid-tied renewable energy source. The system includes a centralized ∞ controller, a multi-agent system, and a distributed control. The centralized ∞ controller generates active and reactive power setpoints. The multi-agent system is integrated into the grid through a grid-tied inverter coupled with an LCL filter. The distributed control controls the multi-agent system and the grid-tied inverter to adjust output power of a multi-agent microgrid storage cooperatively so that they achieve consensus in the energy while providing inertial support.

The invention discloses a multi-parameter MPC two-phase liquid cooling energy efficiency optimization distribution control system and method, and relates to the technical field of liquid cooling energy efficiency optimization. The multi-parameter MPC two-phase liquid cooling energy efficiency optimization distribution control system and method comprises the following steps: S1, collecting and preprocessing two-phase liquid cooling control data; s2, the thermal load level of each area is evaluated periodically, a discrete time linear state space model is constructed, and the thermal load change rate is extracted; s3, the cold distribution amount of each area is quantified periodically, the pump speed and the valve opening target value are calculated, and an adjustment control instruction is generated; and S4, high heat risk and low heat stability areas are recognized, the cooling capacity compensation requirement is evaluated, a cooling capacity enhancement and recovery distribution value is output, and a corresponding control instruction is generated. The problems that an existing two-phase liquid cooling system generally only depends on temperature feedback for adjustment, so that cooling capacity distribution response lags behind, a short-time overheating phenomenon is prone to occurring in a high-heat area, and cooling capacity waste exists in a low-heat area at the same time are solved.

The invention provides an injection molding process quality prediction and process parameter sliding window online optimization method and system. The method comprises the steps of obtaining time sequence process parameter data and batch statistical data; constructing and training a CNN-PCLSTM quality prediction model to output a product quality prediction value; dividing a sliding window in the time sequence process parameter data; constructing a loss function based on a deviation between a product quality prediction value predicted by the CNN-PCLSTM quality prediction model and a preset expected product quality target value, performing iterative optimization calculation based on the loss function, and adjusting a time sequence process parameter set value in the sliding window; updating the process parameter set value in the sliding window obtained through iterative optimization calculation to a control system in the injection molding process; and moving the sliding window and repeating the steps. According to the method, the problems of low quality prediction precision and insufficient optimization efficiency in the prior art are effectively solved.

The invention discloses a self-adaptive temperature control method and device. The method comprises the following steps: acquiring an actual temperature and a target temperature, and initializing a difference value between an actual temperature value and a target value and a change rate of the difference value; mapping between the actual temperature and the target temperature and fuzzy values in fuzzy control is established according to the input quantity fuzzification quantization factor and the output quantity scaling factor; an adaptive coefficient m is established, the calculation formula of m is # imgabs0 #, ts is the target temperature, and ta is the actual temperature; the output quantity scale factor is corrected, and the correction formula is # imgabs1 #; and calculating Kp, Ki and Kd parameters in real time based on the fuzzy control rule table, and carrying out PID (Proportion Integration Differentiation) operation by using the Kp, Ki and Kd parameters so as to realize temperature control. According to the invention, the PID control parameters can be adaptively adjusted, and the PID parameters are automatically set according to the fuzzy empirical rule to achieve the temperature control precision and control speed under different conditions. The stability and the efficiency of the control system are ensured, and the temperature control precision and the control speed under different conditions can be achieved.

The invention relates to the technical field of automatic driving, in particular to an automatic driving obstacle intention prediction and avoidance method and system. The method comprises the following steps: acquiring image data and three-dimensional point cloud data in real time, respectively extracting static features and dynamic features, weighting by adopting an attention mechanism, fusing to form a fused feature vector, inputting the fused feature vector into a plurality of independently trained deep learning models, and obtaining a prediction intention by using a model difference weighted fusion output result. The method comprises the following steps of: setting an association constraint of an operation state and input control based on a prediction intention, establishing an avoidance strategy model by taking path length and time cost as double optimization targets, continuously updating input control in a prediction time domain until an optimization target value is iterated to be minimum, outputting a time sequence of an avoidance strategy, and generating a final execution instruction in combination with a real-time operation state. According to the invention, the bottleneck of a traditional system based on static characteristics and preset rules can be broken through, and the road traffic efficiency is obviously optimized while the traffic safety redundancy is improved.

The invention provides a multivariable decoupling control method and system for an air inlet system of a high-altitude simulation cabin, and the method comprises the steps: firstly building a precombustion chamber cavity thermodynamic differential equation, and building a state equation in combination with a regulating valve first-order inertia model and an engine model; constructing a decoupling matrix by using a differential flatness theory, and converting a pressure and temperature coupling system into an independent control channel; monitoring a system state in real time by using an extended state observer, uniformly estimating internal and external disturbances as total disturbances, and generating a compensation signal; the model prediction controller performs rolling optimization on future time domain control quantity according to deviation between a set value and an actual value, generates an initial instruction in combination with the compensation signal, and converts the initial instruction into an independent valve control signal through a decoupling matrix; the high / low-temperature airflow mass flow is controlled through the opening degree of the adjusting valve, the engine model outputs actual parameters to form closed-loop feedback, and the controller updates the control quantity in each cycle. The multi-variable strong coupling, the dynamic disturbance and the model uncertainty are effectively overcome, the control precision and the parameter robustness are both achieved, and the complex working condition requirements are met.

The invention discloses a combined heat and power generation system control strategy based on an AME-TD3 algorithm, and belongs to the technical field of combined heat and power generation system optimization control. Comprising the following steps: S1, collecting system parameters and initializing a CHP running state; s2, when a target # imgabs0 # value is calculated, an entropy reward mechanism is introduced; s3, realizing exploration and utilization of an adaptive noise adjustment mechanism and a dynamic balance strategy based on Critic network evaluation; and S4, optimizing the control strategy in real time. Aiming at the problem that an existing TD3 algorithm is insufficient in exploratory performance, an entropy correction item is introduced into calculation of a target # imgabs1 # value, structural correction is conducted on a target value function, strategy updating is more efficient, and the effectiveness of overall strategy optimization is improved; aiming at the problem that random noise in a traditional TD3 algorithm cannot adapt to a complex environment, a dynamic self-adjusting noise generation function is added in a strategy updating process, so that the convergence speed is increased, the stability is enhanced, and the self-adaptive adjustment capability of a system to load fluctuation and environmental condition change is enhanced. The method has good expandability.

A method for automated guidance of a vehicle along a specified setpoint trajectory at an actual speed, wherein the setpoint trajectory includes geometric trajectory variables, the method including ascertaining an actual deviation of the vehicle from the setpoint trajectory and outputting the ascertained actual deviation and generating manipulated variables such that the vehicle, in the case of automated control of a drive system and / or a braking system and / or a steering system of the vehicle, moves closer to the setpoint trajectory. The method also includes ascertaining whether an undesirable driving state is present when the vehicle moves closer to the setpoint trajectory, wherein the undesirable driving state is ascertained from the specified setpoint trajectory based on the geometric trajectory variable, and, if the presence of the undesirable driving state is identified, automatically controlling the vehicle based on generated stability variables and / or adapting the setpoint trajectory.

The invention relates to the technical field of environment regulation and control, and discloses a multi-parameter environment closed-loop regulation and control method and system based on an FPGA, and the method comprises the steps: unifying monitoring data to a same space coordinate system through the FPGA according to the monitoring data collected by a sensor network disposed in an environment, analyzing the coupling relation between different parameter data, and obtaining fusion data; based on the fusion data, analyzing the environment crop state through a preset crop state recognition model, simulating the influence condition of different parameter adjustment processes on the crop state, and generating a first control instruction set; analyzing the degree of deviation between the environmental parameters in the fused data and a preset target value, calculating the priority of each control instruction in the first control instruction set, and merging and optimizing the control instructions; the environmental parameters are corrected; according to the application, the regulation and control efficiency can be improved, the environmental disturbance and the crop demand change can be quickly responded through FPGA parallel processing, and the environmental regulation and control efficiency and the regulation and control effect are improved.

The invention discloses a serial cold rolling system thickness and tension control method based on data driving, and relates to the technical field of intelligent predictive control. According to the method, the neural network model is driven by non-autonomous data for prediction, dynamic mapping of tension and thickness parameters is realized through a self-learning mechanism, and high-precision prediction can be completed without depending on real-time measurement data. According to the method, the problems of hysteresis and error accumulation of a traditional measurement link are effectively solved, and deviation transfer in multi-rack cooperative control is effectively inhibited while the prediction efficiency is remarkably improved. A Laguerre function is integrated, parameter dimensionality reduction of a network weight space is achieved through orthogonal projection, the calculation amount is reduced, and the time for reaching a target value is shortened. A multivariable coupling mathematical model of the tension and the thickness of the cold-rolled strip steel is constructed, a Nash equilibrium game optimization strategy is introduced based on a distributed optimal control theory framework, and multi-target collaborative optimization control of the tension-thickness is realized.

The invention discloses an AGV navigation system used in a storage dynamic environment, relates to the technical field of storage robot control, and is used for solving the problem of poor stable navigation in dynamic storage. According to the method, a closed-loop navigation link of state convergence, confidence evaluation, corridors, setting generation and cooperative gating is constructed, state frames are packaged in unified time, and positioning confidence and availability marks are generated based on the consistency of pose predictive quantity and pose observed quantity, so that credible hierarchical control is realized in shielding, light reflection and personnel forklift passing scenes, and the positioning accuracy is improved. A time sequence occupation constraint set is formed through obstacle tracking, a forbidden area mark is fused, a passable corridor boundary is generated, a speed set value sequence and a curvature set value sequence are generated in a corridor, acceleration change rate constraint and steering change rate constraint are applied, and stable driving control and steering control instructions are output. And the way-giving sequence and the recovery sequence are matched, and the execution state and the occupation state are written back, so that the navigation safety, the stability and the operation continuity are improved.

In variants, a method for industrial process control can include: determining AI setpoint values using an AI agent; determining local setpoint values using a local control system; selecting a setpoint source from a set of candidate setpoint sources including the AI agent and the local control system; optionally determining a set of transition setpoint values based on setpoints provided by the setpoint source; and limiting the setpoint values; wherein the industrial system is operated based on the limited setpoint values.

The present application relates to the technical field of thermal power plants, and discloses an intelligent operation method and system for a thermal power plant, a device, and a storage medium. The method comprises: measuring operation parameters of a thermal power plant in real time by means of advanced detection and soft sensing techniques; establishing a fault monitoring model to monitor the operation process of the thermal power plant on the basis of the real-time detected operation parameters, and generating an early warning signal when a fault occurs; performing fault diagnosis, and generating a fault self-healing instruction; and evaluating the operation status of the thermal power plant by means of a performance evaluation model, and then obtaining optimal operation modes and optimal operation parameter setpoints by means of a multi-objective optimization algorithm. The present application achieves high-performance real-time intelligent closed-loop control, improving the adaptability to complex working conditions and the disturbance rejection capability of a thermal power plant production process control system, and the overall performance of a thermal power plant production process; and efficient human-computer interaction is achieved by means of artificial intelligence-based early warning diagnosis and closed-loop self-healing.

The invention relates to the technical field of machine learning, and discloses a learning path generation method based on reinforcement learning, and the method comprises the steps: obtaining the state deviation data of a target object relative to a target value, and constructing an evolution trajectory; extracting a first-order derivative and a second-order gradient of the state deviation to determine a response phase acceleration vector; when the acceleration vector exceeds a threshold value, judging a response phase sudden change and locking a time point; triggering asymmetric sampling space reconstruction of the empirical pool, and executing sampling probability attenuation on historical samples before the time point by using an exponential attenuation operator; according to the method, the logic viscosity of the strategy network to the old response phase is forcibly relieved by using an asymmetric sampling mechanism, the strategy updating lag generated by high-reward historical experience is eliminated, and the strategy remodeling efficiency of the system under the working condition of phase migration is improved.

The invention discloses an AC excitation system self-starting control method and system, and the method comprises the steps: obtaining a rotor current rotation frequency and an actual rotor rotation frequency, and generating a slip frequency; the slip frequency is compared with a preset dynamic threshold value, and the rotating speed given value is dynamically adjusted; inquiring a preset multi-stage sectional type current control curve according to the actual rotor rotation frequency, and outputting a d-axis current given value; pI adjustment is carried out based on the slip frequency to generate a q-axis current given value, and corresponding preset current threshold limit is applied according to the frequency band where the actual rotor rotation frequency is located; performing independent parameter configuration on the magnetic flux current control loop and the torque current control loop by adopting a non-coupling parameter setting algorithm; and in the unit starting process, under the condition that the stable starting constraint condition is met, an optimal starting parameter combination is generated. According to the invention, by dynamically adjusting the rotating speed given value and controlling the current in a segmented manner, the stable self-starting of the alternating current excitation system is realized, and the reliability and efficiency of the starting process are improved.

The invention discloses a superheat degree control method and system for suppressing superheat degree oscillation. The method comprises the steps that the process identification module identifies dynamic characteristics of the superheat degree control system through a transfer function; the prediction control module constructs a state space model according to the dynamic characteristics, performs state prediction based on a state estimation value and a disturbance estimation value, outputs a state prediction vector and calculates a first objective function; the parameter identification self-tuning module carries out self-tuning on the PID control parameters according to the dynamic characteristics and a second objective function; the PID control module outputs an electronic expansion valve driving signal according to the deviation between a superheat degree predicted value in the state prediction vector and a superheat degree preset target value and the set PID control parameter; and the electronic expansion valve driver drives the electronic expansion valve to act according to the electronic expansion valve driving signal so as to adjust the flow of the refrigerant. According to the technical scheme provided by the embodiment of the invention, superheat degree oscillation caused by low superheat degree operation of the refrigerating system is remarkably inhibited, and the system stability is improved.

The invention provides an excitation amplitude automatic closed-loop control system and method for a DLCP test. The system comprises a multi-frequency alternating current excitation and direct current bias voltage applying module which is used for automatically applying alternating current excitation signals and direct current bias voltage signals according to a preset excitation amplitude sequence under a plurality of test frequencies; the PID closed-loop control module is used for performing closed-loop correction on the applied voltage according to the response of the test port of the tested semiconductor device, so that the condition of the applied voltage at the test port meets a preset requirement; the capacitance measurement module is used for acquiring an equivalent capacitance response signal of the tested semiconductor device; the capacitance test data caching module is used for performing associated marking and classified caching on the capacitance test data according to the test frequency, the excitation level and the bias voltage set point; and the main control module is used for coordinating automatic test processes under the conditions of a plurality of test frequencies and different excitation amplitudes and outputting corresponding capacitance test data so as to improve the consistency, stability and automation degree of the DLCP test.

The invention provides a regulation and control method, system and device for a relay valve overrun test and a medium, and belongs to the technical field of air braking systems. The method comprises the steps that pressure response characteristics of a relay valve pipeline system under different opening degrees of a proportional pressure regulating valve are tested, a finite set representing pipeline dynamic response is constructed based on pressure data in a sampling period, and a dynamic matrix model is generated to describe the relation between future pressure output and control quantity; pipeline pressure output at the future moment is predicted based on the dynamic matrix model, an optimized target function is established in combination with the target boosting rate, and the optimal control quantity is solved to adjust the opening degree of the proportional pressure regulating valve in real time; comparing a predicted pressure value with an actual measurement value to obtain an error, dynamically correcting a predicted model parameter by using a weighted error sequence, and adjusting a subsequent control quantity to compensate system hysteresis and external interference; and circularly executing the processes until the pipeline pressure and the regulation and control time reach corresponding target values, and cutting off the gas source.

The invention discloses an automatic pipeline conveying control system driven by liquid level meter signal feedback, and particularly relates to the field of pipeline conveying control, which comprises a data acquisition module, a liquid level state analysis module, a hierarchical control module and a control execution feedback module, the data acquisition module is used for acquiring liquid level height data in the pipeline system and correcting a measurement error; and the real-time liquid level data is compared with a preset liquid level target value, a liquid level deviation value is calculated, and the deviation degree of the current liquid level and the target liquid level is determined. The liquid level meter and the collecting camera are combined, limitation of single equipment is reduced, more accurate and comprehensive data reflecting the liquid level condition can be obtained, the real-time state and the development situation of the liquid level can be accurately known, an accurate basis is provided for follow-up control, when the liquid level is abnormal, countermeasures can be taken in order according to a preset rule, and the accuracy of control is improved. And the failure rate of equipment is reduced, and the maintenance and repair times are reduced.

To provide a control device, a winding device, a control system constructing method, a control system, a control method, a program, and a storage medium that are able to bring the amount of control from a control object closer to a target value.SOLUTION: A control device according to an embodiment controls a winding mechanism capable of winding an object. The control device includes a first control unit, a first calculation unit, and a second calculation unit. The first control unit acquires, from the winding mechanism, the amount of control, which is to be controlled. The first control unit acquires a first amount of operation corresponding to the amount of operation in a case where the amount of control is output from the winding mechanism by inputting the amount of control to a model that has been machine-learned in advance. The first calculation unit calculates a difference between the first amount of operation and a second amount of operation transmitted to the winding mechanism. The second calculation unit inputs, to the winding mechanism, a third amount of operation obtained by adding the difference to the second amount of operation.SELECTED DRAWING: Figure 1

The invention relates to a shaft ECD intelligent regulation and control method and system based on machine learning and closed-loop control, and the method comprises the steps: obtaining original data of drilling parameters, and constructing an ECD prediction model based on the original data; acquiring real-time input data; inputting the real-time input data into the ECD prediction model for prediction to obtain an ECD prediction value; calculating according to the ECD predicted value and the target ECD value to obtain an ECD error value; and selecting a model intelligent regulation and control mode or a PID intelligent regulation and control mode according to the ECD error value to carry out ECD regulation and control. According to the method, the shaft ECD is predicted through the machine learning model, and the drilling parameters are adjusted in real time through the PID controller, so that the ECD is kept within the preset range. The double-layer control system combines the real-time feedback advantage of PID and the predictive capacity of machine learning, it is ensured that variables are accurately regulated and controlled in the drilling process, the ECD is maintained within the safety range, the risks such as stratum fracture and blowout are reduced, and the drilling efficiency and safety are improved.

The invention relates to the technical field of digital twinning, and discloses a process industry digital twinning platform system, which comprises a process mechanism modeling module used for constructing a multi-scale first principle model system according to physical and chemical laws of the process industry; the dynamic coupling engine is used for collecting process data in real time and carrying out coupling solution by combining the first principle model system and the equipment operation state model; the closed-loop optimization control interface is used for carrying out two-way communication with the distributed control system, generating an optimization set value based on a solving result and executing control; and the visual diagnosis module is used for carrying out multi-dimensional analysis, fault root cause tracing and early warning response on process parameter deviation. First principle equations of reaction kinetics, heat transfer theory and the like are directly embedded into the virtual model through the process mechanism modeling module, so that the model has physical and chemical interpretation capability on complex chemical reactions and energy transfer, and high precision is still kept under complex working conditions.

The invention discloses an internet-of-things central air conditioner control method based on a joint regulation and control algorithm model, which comprises the following steps: S1, periodically acquiring temperature, area and use purpose parameters through an internet-of-things temperature controller mounted in a tail end room, and uploading the parameters to an edge computing node; s2, collecting water temperature, water pressure and flow data in real time through an Internet of Things sensor arranged on a pipeline, and uploading the data to an edge computing node; s3, the edge calculation node calculates a load coefficient alpha and a water flow coefficient beta based on the collected information; s4, the control system takes the load coefficient alpha and the water flow coefficient beta as input, and determines a host outlet water temperature set value and a water pump frequency based on a host-water pump combined regulation and control algorithm model; and S5, the host and the water pump are controlled based on the host outlet water temperature set value and the water pump frequency, and dynamic regulation and control between cold and heat supply and energy consumption are achieved. According to the method, dynamic matching of cooling and heating loads and energy-saving optimized operation of the system are realized through accurate acquisition of terminal data and multi-dimensional load modeling.

A controller for performing automated system identification. The controller includes processors and non-transitory computer-readable media storing instructions that, when executed by the processors, cause the processors to perform operations including generating a predictive model to predict system dynamics of a space of a building based on environmental condition inputs and including performing an optimization of a cost function of operating building equipment over a time duration to determine a setpoint for the building equipment. The optimization is performed based on the predictive model. The operations include operating the building equipment based on the setpoint to affect a variable state or condition of the space and include monitoring prediction error metrics over time. The operations include, in response to detecting one of the prediction error metrics exceeds a threshold value, updating the predictive model.

Methods, apparatus and systems for providing secondary frequency regulation support to a power transmission system. In some embodiments, a real-time central controller requests current status data from a power distribution network which includes a plurality of distributed energy resources (DERs) and at least one substation battery energy storage system (BESS). The real-time central controller also receives an Automatic Generation Control (AGC) signal from a system operator, calculates a mismatch value, determines that the absolute value of the mismatch value is greater than a predetermined tolerance, and sets an optimal setpoint value for each of the plurality of DERs and an optimal setpoint value for the BESS. In some implementations, the real-time central controller next transmits a secondary frequency response that includes the optimal setpoint values to each of the plurality of DERs and the optimal setpoint value to the at least one BESS, thus providing secondary frequency regulation support.

The invention discloses a motor system energy-saving optimization automatic control system based on industrial Internet of Things, which relates to the technical field of automatic control, and comprises a data acquisition module, a load decoupling module, an aerobic prediction module, a biochemical load disturbance index calculation module and a control module, the system comprises an operating parameter prediction module, a disturbance index prediction module, a decision optimization module, a model correction module and an oxygen demand prediction module, wherein the operating parameter and the disturbance index are combined to predict an optimal dissolved oxygen set value; the decision optimization module generates an optimal motor rotating speed control instruction according to the disturbance index and the set value; and the model correction module calculates a deviation factor according to the set value and real-time concentration and performs online correction when a threshold value is exceeded. Through cooperation of multiple modules, accurate, energy-saving and self-adaptive regulation and control of the aeration process are realized, loads can be accurately distinguished, the treatment effect and energy consumption can be balanced, long-term stable operation is guaranteed, and the energy consumption of the system is remarkably reduced.

The invention discloses a cross-industry multi-source fault intelligent research and judgment method and system based on a large model, and relates to the technical field of large models, and the method comprises the steps: calling a historical fault record of a power transmission line, deploying a sensor on the power transmission line, and obtaining target data corresponding to each fault position in each physical type; obtaining an associated physical type and an associated function corresponding to each fault type; extracting all detection points on the target power transmission line to obtain first target values of the detection points; extracting a management and control area where personnel are limited to enter in the power transmission line, and obtaining a second target value of each detection point; setting a weight to obtain a total target value of each detection point; and obtaining a first judgment criterion and a second judgment criterion, and judging whether to carry out early warning prompt on the selected sampling point or not. According to the method, the fault record and the control area are analyzed, so that the current sampling point is more reasonable and representative, the power transmission line fault is intelligently researched and judged, and the reliable judgment on the power transmission line is effectively improved.

Method for strapping objects, comprising the following steps: • Transporting an object (7, 7') by means of at least one conveyor device (13, 14) to a strapping station, • Forming at least one loop of a strapping band (2) around the object (7, 7') by means of a strapping device, • Connecting the ends of the loop together, • Detecting the position of the object (7, 7') by at least one position detecting means (17, 18, 19), • a detection device detects the conveying direction and conveying distance of the conveying device (13,14), • the signals of the detection device and the position detection means (17, 18, 19) are transmitted to a control unit (11), • the control unit (11) controls the conveyor device (13, 14) on the basis of the transmitted signals in such a way that the object (7, 7') is transported into at least one strapping position in which the loop of strapping band (2) is formed on the object (7, 7') at a predetermined loop position, • at least one property of the object (7, 7') is recognized by a recognition device (20), • Data specifying the property of the object (7, 7') are forwarded to the control unit (11) and • the control unit (11) determines at least one target value for the loop position from the data specifying the property of the object (7, 7'), characterized by the following steps: • Storing data sets with target values ​​for the strapping parameters in a data memory (29) which is connected to the control unit (11); • Selection of a data set with target values ​​for the strapping parameters by the control unit (11) for each strapping process, wherein the data set is selected by the control unit (11) on the basis of the data specifying the at least one property of the object (7, 7').

The invention relates to a variable-speed pumped storage group power system low-frequency oscillation self-adaptive control method, and relates to the field of power control, and the method comprises the steps: processing power system operation data and energy storage group power generation monitoring power through a mode recognizer, and outputting a first low-frequency oscillation mode; when the mode belongs to the low-frequency abnormal oscillation mode, a water turbine mechanical energy prediction network is called, a mode recognizer is combined, an optimization algorithm is executed on unit control parameters, and the recommended unit guide vane opening degree, the recommended unit operation frequency and the recommended unit rotation speed set point are obtained; controlling a water turbine according to the recommended unit guide vane opening and the recommended unit operation frequency, and controlling an alternating current excitation device according to the recommended unit rotation speed set point. The technical problem that in the prior art, low-frequency oscillation control is achieved depending on adjustment of physical components, and consequently adaptability is poor is solved.

The invention relates to the technical field of automatic control theory application and data center thermal management, and discloses a data center emergency refrigeration control method based on state space model prediction, which comprises the following steps: establishing a thermodynamic cycle state space equation; the refrigeration flow set value change rate and the water return side pressure change amount are obtained, and the dynamic heat exchange sensitivity is determined; monitoring feedback current characteristics of the driving unit, inverting cooling path resistance distribution and updating gain matrix elements; calculating a state prediction information sequence according to the temperature vector prediction deviation, and adjusting a time domain step length; the dynamic heat exchange sensitivity is introduced into prediction logic, controlled variable dynamic pressure retardation is compensated, an emergency refrigeration frequency control instruction is output, fluid fluctuation is converted into sensitivity compensation of a logic level, non-linear dynamic pressure damping of the emergency working condition is effectively impacted, the problems of control overshoot and logic instability caused by physical lag are solved, and the reliability of the system is improved. And the refrigeration response precision at the emergency conversion moment is improved.