335 results about "Control variable" patented technology

The invention relates to the technical field of life cycle management, in particular to a catalyst industry chain-oriented full life cycle traceability method and system, and the method comprises the steps: obtaining the state attribute data of the same batch of recovered products, and carrying out the analysis to obtain an efficiency feature vector; inputting the efficiency feature vector into a preset quality evaluation model for matching, and identifying quality deviation data of the recycled product; the batch identity codes are analyzed, and resume data of the production full life cycle are obtained; a multi-dimensional data association model is constructed, quality deviation data is taken as a target variable, resume data is taken as an input feature, an influence weight coefficient and an attribution confidence index of each production control variable on the quality deviation are analyzed, and key control elements are screened out; and performing production regulation and control optimization operation based on the key control elements, calculating target parameter offset for correcting quality deviation, dynamically calculating a dynamic tolerance constraint range of a production control variable in combination with an attribution confidence index, and packaging into an execution management and control protocol.

A valve timing control device includes: a unit for calculating a target valve timing for one of an intake valve and an exhaust valve in accordance with an operational state; a unit for detecting an actual valve timing of one of the intake valve and the exhaust valve; an actual valve timing control unit for generating a controlled variable for the VVT based on a difference between the target valve timing and the actual valve timing; a unit for learning a learning value of the controlled variable required for maintenance of the actual valve timing based on an integral correction value; and a unit for detecting a malfunction in a VVT from an amount of a shift in the actual valve timing when learning is not completed yet, and for detecting a malfunction in the VVT from the difference when learning is already completed.

The invention discloses a standard system applicability evaluation method and system based on multi-source data, and relates to the field of standardization, and the method comprises the steps: collecting the historical operation data of equipment, and dividing the equipment into an experiment group equipment group and a control group equipment group; the method comprises the following steps: preprocessing historical operation data of equipment, and respectively constructing a performance index value sequence for quantitatively reflecting the achievement condition of a target standard for a technical target for an experimental group equipment group and a control group equipment group; and on the basis of the performance index numerical value sequences of the experimental group equipment group and the control group equipment group, a net effect value and statistical confidence of a target standard are calculated by adopting a double difference model, and a unit model and a geographical climate region are added into the double difference model as fixed effect control variables. According to the method, the corrected net effect value and the intermediary centrality score are fused into the comprehensive index, so that multi-dimensional dynamic evaluation combining data driving, causal identification and knowledge value of standard applicability is realized.

The invention discloses a reinforcement learning-based slurry balance shield construction attitude settlement collaborative optimization method and system, electronic equipment and a storage medium. The method comprises the following steps of: constructing state and action characteristics; a control strategy modeling step: constructing a composite reward function which takes incision pressure, partition propulsion pressure, tunneling speed and the like as control variables and fuses ground surface settlement and attitude control performance; designing a control strategy model based on reinforcement learning; a control strategy training step; a control parameter output step; in the shield tunneling process, control parameter setting is dynamically generated through current state information, and cooperative control over settlement and posture is achieved. According to the slurry balance shield construction posture settlement collaborative optimization method and system based on reinforcement learning, the electronic equipment and the storage medium, the adaptive capacity and self-learning capacity of the system can be improved, and the automation, intelligence and construction safety level of the slurry balance shield construction process can be improved.

The invention discloses an electric linear servo system control method based on cascade LESO-DISMC. The method comprises the following steps: firstly, establishing a complete mechanism model of a permanent magnet synchronous motor servo loading system; then designing DOB interference observer real-time compensation by adopting a LuGre friction model and a gap dead zone model; a feedforward control quantity is constructed by utilizing a measurable displacement signal, and redundant torque is inhibited; designing a dynamic integral sliding mode controller to ensure global stability; a high-low bandwidth LESO is constructed, and both fast tracking and smooth filtering are considered; and finally, trajectory optimization is carried out, a smooth trajectory is generated by adopting polynomial interpolation for the step and triangular wave signals, and mechanical vibration is inhibited. According to the method, a LuGre friction model is adopted to compensate nonlinear friction, cascade linear LESO and sliding mode control are combined, a track is optimized through an S-shaped curve, and the problem of'differential explosion 'is solved; according to the method, instruction smoothing, core control and friction compensation are fused, and the anti-interference capability and the loading precision of the system are improved.

The invention discloses a helicopter prediction control method based on a physical mechanism neural network and a helicopter, and the method comprises the steps: building a helicopter prediction model, so as to describe a relation between a future state of the helicopter and a current state quantity and a control variable; constructing a predictive control model based on a physical mechanism neural network; generating a control quantity sequence by adopting a forward reasoning process of the neural network; according to the control quantity sequence and the state quantity, adopting the helicopter prediction model to perform multi-step iterative evolution in an autoregression form to obtain a predicted helicopter state trajectory; calculating a loss value by adopting a loss function of an embedded physical mechanism according to the expected instruction and the predicted helicopter state trajectory, and performing back propagation on the neural network to update neural network parameters; and inputting the state quantity obtained in real time and an expected instruction into the trained prediction control model to obtain an optimal control sequence, and applying the first control quantity to realize rolling time domain closed-loop control.

The invention relates to a PTFE extrusion model prediction control method and system based on a time convolution network. The method comprises the steps of collecting multi-dimensional time sequence data of an operation variable, a state variable and a controlled variable of a pushing production line in real time; performing data preprocessing; constructing a supervised learning sample by adopting a sliding time window; training the time convolution network; constructing a cost function, and setting constraint conditions; acquiring the actual die orifice outer diameter, the traction speed, the pushing speed and the pushing pressure at the current moment, and inputting the actual die orifice outer diameter, the traction speed, the pushing speed and the pushing pressure into the trained time convolutional network to obtain a prediction result of the future die orifice outer diameter; based on the prediction result, solving the cost function to obtain an optimal control sequence used for offsetting the die orifice outer diameter deviation caused by abnormal fluctuation of the pushing pressure; real-time data are collected again, the prediction and optimization process is repeated, and rolling prediction and optimization control are achieved. The problem that the outer diameter is out of tolerance due to fluctuation of raw materials is solved, and unmanned and intelligent production is achieved.

The invention relates to the technical field of heat pump drying control, and discloses a dynamic identification control method of a closed heat pump drying system based on waste heat recovery. The method comprises the following steps: establishing a multi-level parameter monitoring network by arranging a sensor array, and continuously collecting system operation parameters to form time sequence data; constructing a waste heat distribution characteristic spectrum, and mapping the collected parameters to a thermodynamic state space to identify a coupling relationship among the parameters; analyzing a heat transfer path to determine a key control variable, and establishing a dynamic weight adjustment mechanism to generate a parameter priority sequence; and a multi-target coordination control strategy is made based on the sequence and the real-time operation state, and an actuator control instruction is output. According to the method, through global energy state recognition and dynamic priority adjustment, adaptive optimization operation of the system under different working conditions is realized, and the energy utilization efficiency and the control quality are improved.

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.

Systems and methods may analyze a simulation model that includes model elements configured to operate as variants. The variants may be controlled by variant conditions that may include variant control variables. A group of variant configurations may be defined for the model where each variant configuration may include a different set of the variant control variables. For each variant configuration, the systems and methods may determine which model elements configured to operate as variants are active and which model elements are inactive. The systems and methods may also determine which model elements, if any, are always active, are never active, or are sometimes active. A visualization tool may present the results of the variant configuration analysis in the form of a heatmap, a textual report, an annotated version of the simulation model, or graph, among other forms.

A system for managing well site operations comprising a well site operations module, a chain of blocks of a distributed network, and a sensor bank and control module. The operations module generates earth model variables using a physics model, well log variables or seismic variables, or both, and a trained AI / ML algorithmic model. The chain of blocks comprises a plurality of subsequent blocks. Each subsequent block comprises a well site entry and a hash value of a previous well site entry. A well site entry comprises transacted operation control variables. The well site operations module generates production operation control variables or development operation control variables from earth model variables. The well site entry can also include transacted earth model variables and sensor variables. The sensor bank and control module provides well log variables and the operations module couples control variables to the control module to control well site equipment.

The invention discloses a 1Ds-MPC control method and device for MMC multi-target step-by-step optimization, and a medium, which decouple the traditional highly-coupled MMC multi-target control problem into three independent control of alternating current tracking, circulating current suppression and capacitor voltage, and construct a single-peak one-dimensional cost function by taking the number of upper bridge arm input sub-modules as a unique search variable, so as to realize the multi-target step-by-step optimization of the MMC. Gradually shrinking the search interval by adopting a golden section proportion in the current control period, and determining a temporary input quantity; by taking the temporary input quantity as an initial value and keeping the circulating current predicted value to be zero as a target, applying error compensation sub-modules with the same quantity to the upper and lower bridge arms at the same time to obtain a final input quantity; and by taking the final input quantity as a reference, only performing local sorting and switching decision on the sub-module groups exceeding a set voltage fluctuation allowable range, and generating a switching pulse. The technical problems of complex weight factor design, control variable coupling and the like in the traditional model predictive control of the modular multilevel converter are solved.

The invention belongs to the field of vehicle trajectory tracking control, and discloses an intelligent vehicle trajectory tracking control method and system based on differential programming, and the method comprises the steps: constructing a vehicle dynamics model; a reference trajectory time-varying curvature is introduced, delay of a steering execution mechanism is expressed as pure delay plus a first-order inertia link, and construction of a road tracking model is completed; assuming that the system is in a steady state, calculating reference values of a state quantity and a control quantity; constructing an MPC optimization problem, converting the MPC optimization problem into a QP problem, and calculating to obtain an MPC loss value; constructing a differential programming training calculation network diagram; a hypercube is used for sampling the state quantity of a vehicle, the driving speed and the time-varying reference items of a road, and construction of a network training initial state data set is completed; carrying out MPC trajectory tracking task integration, and constructing a physical model in network training; using a full-link layer network, adding tire slip angle soft constraints, completing the construction of a strategy network, carrying out end-to-end network training, and completing the control of a turning angle instruction. According to the method, the MPC online solving optimization problem can be avoided.

The invention discloses an intelligent anti-interference cascade control method for a rectifying tower, an intelligent anti-interference cascade control device for the rectifying tower, intelligent anti-interference cascade control equipment and a computer readable storage medium. The method comprises the steps that S1, a main controlled variable, an auxiliary controlled variable and at least one feedforward disturbance variable in the rectifying process are monitored in real time; s2, according to the variable quantity of the feed-forward disturbance variable, a feed-forward compensation quantity is calculated through a dynamic feed-forward compensation model; s3, according to the deviation between the main controlled variable and the set value and the deviation between the auxiliary controlled variable and the set value, feedback control quantity is obtained through cascade control loop calculation; s4, adding the feedforward compensation quantity and the feedback control quantity to obtain a final control instruction; and S5, after amplitude constraint processing and change rate constraint processing are carried out on the final control instruction, the final control instruction is output to an execution mechanism for controlling manipulated variables of the rectifying tower. Product quality stability is improved, disturbance recovery time is shortened, and energy consumption is reduced.

The invention discloses a federated learning method and system for heterogeneous data of the Internet of Vehicles, and relates to the technical field of intelligent transportation. The method comprises the following steps: a roadside unit sends a current global model and a global control variable to a vehicle participating in training; the vehicle executes local training combined with a client drift correction mechanism through local data and a differential privacy stochastic gradient descent algorithm, and generates local model update; the vehicle calculates a noise multiplier of the next round of training through a personalized self-adaptive differential privacy strategy according to the loss value of the current round of training; the vehicle judges whether the current round of model updating is effective or not based on selective uploading; and if the update is valid, the vehicle uploads the model increment and the local control variable increment to the roadside unit, and the roadside unit updates the global model and the global control variable after aggregation. According to the invention, systematic innovation is carried out from three levels of privacy protection, model optimization and communication strategies, and a solution is provided for constructing efficient, safe and reliable federal learning in an Internet of Vehicles scene.

This invention discloses a control method for the thermal system of a belt sintering mill based on digital twins, comprising: collecting pellet production process data; correcting and fitting the collected pellet production process data to create a mechanism model for the physicochemical changes during pellet sintering; reducing the mechanism model to a low-order mathematical model, and then, combined with the pellet sintering heat transfer calculation formula, using pellet sintering quality indicators and minimum energy consumption during sintering as constraints, solving for the values ​​of controlled variables at different time points in each process stage of pellet sintering; based on the Urgench equation, solving for the values ​​of controllable variables using the controlled variable values, and sending the controllable variable values ​​to the programmable logic controller (PLC) of the belt sintering mill thermal system, enabling the PLC to automatically control the belt sintering mill thermal system to achieve minimum energy consumption sintering. This invention ensures both pellet production quality and achieves the lowest overall production energy consumption.

The invention discloses a multi-modal practical dynamic security domain construction method and system based on the maximum Lyapunov exponent theory, and the method comprises the steps: obtaining the measurement information of the active power, the power angle, the angular velocity and the like of a generator node from an actual power system, and determining a fault line and a fault state corresponding to a required dynamic security domain; transient simulation of an initial operation point of the system is carried out to obtain a transient stability condition and a power angle leading cluster of the system; for generators in the leading cluster S, calculating to obtain a maximum Lyapunov exponent characteristic value and related characteristics of a control variable, and solving the maximum Lyapunov exponent sensitivity; and constructing a multi-modal practical dynamic security domain based on the maximum Lyapunov index by combining the upper limit and the lower limit of generator output and the output limit of a balancing machine. According to the method, accurate transient stability evaluation before a fault occurs is realized, the application range is expanded, and the limitation that the stability margin cannot be quantified by track stability judgment is changed.

The invention provides a fault prediction and self-protection method and system for heave compensation of a diving bell, and the method comprises the following steps: obtaining the displacement of a hydraulic cylinder of a heave compensation device and the pressure data of an energy accumulator, constructing a hysteresis loop, converting the hysteresis loop into a feature field image, and analyzing and calculating a topological feature descriptor through geometric moment. By performing inverse frequency domain analysis on the pressure data, a friction texture signal is separated and an energy entropy value of the friction texture signal is calculated. Mapping the topological feature descriptor to a control variable space defined by a nonlinear cuspidal point mutation model, and calculating a geometric distance from a current state point to the boundary of a model bifurcation set; and solving an instability prediction index by combining the distance and the energy entropy. And when the index exceeds the limit, the system triggers variable damping control and gradually locks the hydraulic cylinder to realize self-protection. According to the method, the technical problem that the sudden instability failure of the system caused by multi-factor coupling is difficult to warn in advance by a traditional method is solved.

This invention relates to a PID closed-loop fast allocation control method based on virtual measurement, belonging to the field of automatic control technology. The method involves: receiving a general command from the upper level as the setpoint for PID allocation; the controlled variable is the final value of the sum of the output values ​​of all allocated devices after filtering; and adaptively allocating the proportional gain of the PID controller. K P and integration time T i The value is the number of devices put into automatic operation. A , Lock-up BI , Lockdown BD The calculation function value of the set is used to ensure consistent speed, stability, and quality of the allocation control under different conditions of increasing or decreasing input and lockout. The allocation PID control output is expressed as a percentage. After each device receives the control output value of the allocation PID, it is multiplied by its respective allocated rated capacity value to convert it into an engineering quantity value. Then, a manually set bias value is added to obtain the final control output value of each device, achieving flexible increase or decrease while meeting the overall command requirements. This invention has the advantages of simple, fast, flexible, and reliable allocation control.

This invention relates to the field of Automated Guided Vehicle (AGV) navigation technology, and proposes an AGV path correction control method based on an industrial control system. The method includes: generating multiple candidate correction control variables for adjusting the AGV's movement; analyzing the path deviation value of the AGV along the target path from its current position under each candidate correction control variable, to determine the number of correction events required for the AGV to move along the target path from its current position using the path deviation value; using the minimum number of correction events as the optimization objective, determining the constraint range using the current position, current lateral deviation, and current heading deviation, and selecting the target correction control variable from the candidate correction control variables; and sending the target correction control variable to the AGV's actuator to perform path correction control on the AGV. This invention enables effective control of the number of corrections.

This application provides a vehicle control method, device, electronic device, and medium, relating to the field of autonomous driving technology. It constructs a three-part loss function comprising decision variables, state variables, and control variables, and sums the outputs of these three components at each time step to obtain a first sum. The first sums from multiple time steps are then accumulated to form a unified target sum. Under the constraints of a first vehicle's dynamic mathematical model and first constraints, a sequence of driving variables that minimizes this target sum is jointly solved. Finally, the vehicle is directly controlled using this optimal target driving variable sequence. This effectively bridges the coupling channel between discrete decision-making (i.e., lane decision) and continuous trajectory planning, enabling lane decision-making and trajectory generation to work synchronously and collaboratively under the same optimization objective. This improves the safety, driving efficiency, and ride comfort of autonomous vehicles in complex traffic scenarios, achieving superior overall driving performance compared to traditional fragmented algorithm designs.

A description is given of a control device (100) for the longitudinal and / or transverse guidance of a vehicle (102), in particular a utility vehicle, in relation to at least one further vehicle (104) in a vehicle group (106). The control device (100) comprises a determination unit (108) for determining a vehicle air resistance, FL, and a further vehicle air resistance, wFL. The control device (100) furthermore comprises a control unit (110) for controlling a control variable in the vehicle group (106). The control device (100) furthermore comprises a storage unit (112) for storing the determined FL and wFL in association with the controlled control variable in a characteristic diagram. The control unit (110) varies the control variable. A computing unit (114) of the control device (100) calculates an optimizing control variable. The control unit (110) furthermore controls the control variable in accordance with the optimized control variable.

This invention provides an intelligent operation and maintenance system for prefabricated substations based on big data, belonging to the field of prefabricated substation operation and maintenance technology. The data access unit unifies the time alignment of electrical operation, environmental monitoring, and historical operation and maintenance data, constructing a constrained time-series data structure based on equipment topology; the state evolution modeling unit extracts multi-scale features, generating three sets of evolution parameters: impedance, capacity, and load; the control variable reconstruction unit generates enhanced control input vectors through component mapping and iterative feedback; the real-time control execution unit outputs reactive power regulation and voltage control commands accordingly; and the human-machine feedback unit embeds manually corrected information and updates control parameters. This invention solves the problems of lack of topological constraints in multi-source data and the difficulty of embedding human experience into automated control, improving the intelligence level, control accuracy, and adaptability of prefabricated substation operation and maintenance.

The invention discloses a data center intelligent liquid cooling system based on multi-source data fusion and a control method thereof, and relates to the field of intelligent control. The method comprises the following steps: constructing a feature vector of each water cycle; performing clustering analysis on the feature vectors, and classifying the water circulation according to a clustering result; an optimization control model of the clustering category to which the water circulation belongs is constructed, and the optimization control model takes the characteristic parameters as state variables, takes the water inlet temperature adjusting quantity and the water inlet quantity adjusting quantity as control variables, and takes the minimum equipment temperature difference and energy consumption as optimization targets; performing optimization control on the control variables by using a model predictive control algorithm; solving the optimization control model by using a nonlinear optimization solver to obtain an optimal control law; according to the optimal control law, the water inlet temperature and the water inlet amount of each water circulation are adjusted, so that the equipment temperature in the same water circulation is kept at the same level, the equipment temperature uniformity is improved, the PUE value of the data center is reduced, and compared with a traditional liquid cooling system, more energy is saved.

The invention relates to a low-temperature propellant temperature control method based on real-time pressure feedback of a storage tank on a rocket, which comprises the following steps of: acquiring a pressure value, a temperature value and flow of the storage tank in real time to determine whether the pressure value is within a set pressure range; the pressure value of the storage box is adjusted by executing control strategies such as adjusting the temperature set value, and the first set temperature is obtained; when the pressure value is within a set pressure range, determining an optimization target of the state prediction model, inputting the pressure value, the temperature value and the flow into the state prediction model, obtaining a target control variable and a second set temperature of the state prediction model when the optimization target is met, determining a target set temperature according to the first set temperature or the second set temperature, and performing state prediction according to the target set temperature. The target adjustment information including the target set temperature is obtained, and the temperature set value in the filling process is adjusted according to the target adjustment information, so that the low-temperature propellant is always in the optimal temperature state in the filling process, and the filling quality and efficiency are improved.

The present application relates to a multi-control variable control method of a polycrystalline silicon reduction furnace based on a positive mechanism model, comprising the following steps: S1, collecting and calculating the reaction state, i.e. the real-time deposition rate and the heat loss rate of the reaction; S2, inferring the control parameters: determining the control parameters in the PID control process based on the difference between the real-time deposition rate curve and the target deposition rate curve, wherein the real-time deposition rate curve is generated based on the real-time deposition rate of the reaction progress sequence, and the target deposition rate curve is a preset curve; S3, performing the PID control process: using the control parameters in step S2 to perform PID control on the hydrogen input and the reaction current; the present method determines the optimal deposition rate curve through data analysis, and calculates the deposition rate in real time during the control process. The current heat proportion in the furnace is calculated by using the thermodynamic formula, and the hydrogen and the current are precisely regulated and controlled by combining the proportion with the deposition rate difference, which significantly improves the stability and accuracy of the production process.

The application provides a generalized predictive control method and controller based on a Kalman filter observer, wherein a Kalman filter model and a predictive control algorithm are initialized according to information such as a predictive control model, initial parameters of the Kalman filter, and initial parameters of the generalized predictive control; a contraction factor is introduced to correct the accuracy of a Kalman filter reference model; the output of the Kalman filter model is observed based on the control output and the measured controlled variable at the previous moment, and the Kalman filter model is updated; the output of the Kalman filter model is used to replace the measured controlled variable to perform bias calculation in the predictive control; the generalized predictive control calculation is performed based on the set value of the controlled variable and the predictive control bias, so that the final control variable is obtained and is sent to a control device for execution. The system fluctuation or even shock caused by model mismatch can be effectively inhibited, the dynamic overshoot of the controlled variable is reduced, the control convergence time is shortened, the dynamic process curve of the controlled variable is more gentle, and the stability of the predictive control is improved.

A method for controlling an air-conditioning unit which air-conditions the interior of a vehicle includes using a control facility to control the air-conditioning unit in order to air-condition the interior of the vehicle. In order to improve the operation of the air-conditioning unit, a computing facility forms a model using an artificial neural network, the model being configured to ascertain a control variable to be output by the control facility using influencing variables which have an influence on the climate of the interior. A computer program which represents the model is used on the control facility in order to control the air-conditioning unit. An apparatus, a computer program, a computer-readable medium and a vehicle are also provided.

The invention provides a control method and device of an energy storage power station, electronic equipment and a storage medium. The method comprises the steps of obtaining current time; matching the current time with the running time corresponding to the running instance to obtain a target running instance; acquiring a first target parameter of the energy storage power station acquired based on the acquired data type configured by the global operation instance; determining a parameter value of a control variable for executing the target working step on the basis of the target parameter under the condition that the limiting condition for executing the target working step is satisfied on the basis of the target parameter; and controlling the target energy storage unit to execute the target working step based on the parameter value of the control variable for executing the target working step. According to the method provided by the embodiment of the invention, the working steps, the limiting conditions, the control type and the control variable configured by the running instance, so that the control scheme can adapt to various different application scenes, and the control efficiency is improved. And the control flexibility and adaptability of the energy storage power station are improved.

Methods and systems for providing computer implemented services are disclosed. To provide the services, control variables may be obtained and used to manage operation of the distributed system. The operation may be proactively managed by enforcing data placement standards throughout the system based, at least in part, on the control variables. The enforcement may be performed by identifying preferred placement locations based on the control variables, policies, and / or other factors.