772 results about "System dynamics" patented technology

The invention provides an equipment fault early warning method based on a neural network, a medium and equipment, and the method comprises the steps: collecting equipment state information at regular time, recognizing abnormal equipment, obtaining operation parameters and structure parameters of the abnormal equipment and a layout schematic diagram of a system where the abnormal equipment is located, constructing a dynamic coupling topology link, and extracting associated parameters; verifying and generating a first error weight in combination with a physical kinematics equation, generating a second error weight by matching historical fault data, and comprehensively generating fault state information; a detection mode is switched based on risk level evaluation, a system dynamic coupling topology link, associated parameters, abnormal equipment information and fault state information are input into a neural network model, fault link information is output, and finally first early warning information and second early warning information are generated according to a dynamic detection mode to realize two-stage early warning. According to the method, the fault conduction relation between equipment is modeled through the graph neural network, and fault path prediction and systematic risk early warning are realized.

The invention discloses a data analysis platform management method and system, and belongs to the technical field of computer data processing, and the method comprises the steps: obtaining multi-source heterogeneous data from a medical information system; establishing a cross-system data association model, performing dynamic feature extraction and weight distribution on multi-source heterogeneous data, and outputting a resource supply-demand relationship graph; analyzing the resource supply-demand relation graph, and identifying business conflicts in medical resource scheduling; generating a conflict solution of the business conflict based on a multi-objective optimization algorithm, and generating an execution instruction; and issuing an execution instruction to the target medical system, verifying an execution result of the execution instruction, and updating system parameters. According to the method, a cross-system dynamic modeling and multi-objective optimization strategy is adopted, and a closed-loop verification mechanism and a multi-source heterogeneous data fusion technology are combined, so that intelligent prediction, accurate scheduling and self-learning optimization of medical resource conflicts can be realized, full-process traceable execution of high-value consumables is ensured, and the resource utilization efficiency and the emergency response capability are comprehensively improved.

The invention discloses a hybrid energy storage system optimization scheduling method based on AI intelligent regulation and control, and relates to the technical field of hybrid energy storage, and the method comprises the following steps: collecting real-time data, and carrying out the cross verification of data consistency through a multi-source data fusion technology; and identifying data abnormity caused by sensor faults, communication delay and environmental interference by combining adaptive threshold detection with a statistical analysis method, and eliminating abnormal data. According to the method, energy storage scheduling is optimized through data cleaning, time sequence prediction and reinforcement learning, and the intelligent management and dynamic adaptive capacity is improved. Multi-source data fusion and anomaly detection are adopted to ensure data accuracy, energy consumption is predicted by means of LSTM and Transform, and an energy storage strategy is optimized in advance. By combining reinforcement learning and system dynamic adjustment charging and discharging, the photovoltaic consumption rate is improved, the electricity purchasing cost is reduced, the SOC is intelligently controlled to be 40%-80%, and the service life of the battery is prolonged. Meanwhile, the system stability is improved through anomaly detection and correction, and the maintenance cost is reduced.

The invention discloses a city updating intelligent expert system architecture and method based on a large language model, and relates to the technical field of city planning and construction. The knowledge base management module supports efficient retrieval and application of professional domain knowledge; the expert agent group comprises a user-defined core expert and a system dynamic supplement expert; the expert team collaborative decision-making module is used for executing multiple rounds of hierarchical collaborative decision-making processes; the meta-agent module is responsible for project feature analysis and expert team dynamic configuration; the auxiliary agent module comprises a host agent which is responsible for guiding the decision making process; the recorder agent generates a decision summary report; the verification agent is responsible for verifying and evaluating the professional ability of the expert agent; and the scoring agent evaluates the decision quality. According to the method, the problems of dispersed professional knowledge, low cross-domain expert cooperation efficiency, high decision-making cost and the like in the existing city updating decision-making process are solved, and the decision-making efficiency and quality of city updating early-stage planning are comprehensively improved.

The invention discloses a territorial space planning index dynamic evaluation model, and particularly relates to the field of territorial planning, and the model comprises a multi-source heterogeneous data fusion module, an index system construction module, a dynamic adjustment model construction module, a scheme evaluation and optimization module, and a model application and dynamic updating module. According to the method, various spatial data are integrated through a multi-source heterogeneous data fusion technology, and the data quality is ensured by adopting a three-stage cleaning assembly line and an intelligent correction algorithm; the method comprises the following steps: constructing an index system based on six core contents of territorial space planning, and determining an index weight by applying a Delphi method and an analytic hierarchy process to realize adaptive adjustment of an index threshold; the model adopts time sequence analysis, regression analysis and a machine learning algorithm to predict a development trend, a dynamic feedback mechanism is established in combination with a system dynamics method, and an evaluation scheme is quantified through an analytic hierarchy process; the model performance is evaluated by adopting indexes such as accuracy rate and precision rate, and parameters are continuously optimized based on an evaluation result; finally, the model is integrated to a service system to realize real-time monitoring, and dynamic updating is realized through three mechanisms of data driving, performance triggering and regular maintenance.

The invention is an advanced deep learning system that combines a latent transformer core with a latent dynamics analyzer. This system processes input data into latent space vectors, which are then analyzed in parallel for both prediction and dynamic modeling. The latent transformer generates short-term predictions, while the latent dynamics analyzer derives equations of motion describing the underlying system dynamics. By integrating spectral analysis and change detection, the system can identify significant shifts in behavior, particularly useful for complex systems like financial markets. The invention enables more accurate predictions, interpretable insights, and early detection of regime changes. Its end-to-end training approach ensures all components work harmoniously, balancing predictive accuracy with physical plausibility and interpretability.

The invention relates to the technical field of robot control, in particular to a force control method and system based on an impedance model for a rope-driven robot. Comprising the steps of system dynamics modeling and parameter identification, real-time axial compression sensing and model updating, Cartesian space impedance model design, adaptive impedance parameter adjustment, inner ring position control based on feedback linearization, force error compensation and expected trajectory generation, real-time control circulation and anti-interference processing and experimental verification and iterative optimization. According to the method, through real-time axial compression sensing and equivalent Young modulus dynamic updating, the problem of model mismatch caused by material rigidity sudden change in the phase change process of the hyperelastic material is solved. Specifically, a traditional method depends on static material parameter calibration, and real-time compensation of the nonlinear characteristic of the material is achieved through online estimation of the axial compression amount of the main beam and iterative correction of the Young modulus.

The invention discloses a multi-unmanned ship cluster cooperative tracking control method based on data driving, and belongs to the field of unmanned ship cluster intelligent control. The method comprises the following steps: firstly, constructing a communication topology for cluster distributed cooperative control of multiple unmanned ships; constructing a single unmanned ship speed and course system dynamic linearization data model with interference terms based on unknown external interference and nonlinear uncertainty; constructing a smooth anti-interference trajectory tracking algorithm of a single unmanned ship tracking dynamic target point; constructing a pseudo-Jacobian matrix estimation and distributed disturbance compensation controller for distributed model-free adaptive control of the multiple unmanned ships; constructing a reset algorithm and a controller of an actual unmanned ship; and finally, multi-unmanned ship cluster cooperative tracking control is realized. The method breaks through the limitation of a traditional control method, does not need to depend on complex system model information, only utilizes input and output data of the system, and achieves a complete data-driven intelligent control strategy.

The invention discloses a shield tunneling machine axis predictive control method and system, electronic equipment and a storage medium, the shield tunneling machine axis predictive control method is based on data driving modeling, system dynamic characteristics contained in shield tunneling axis control related data are fully excavated, the predictive control precision is high, and the predictive control precision is high. A GRU (Gated Recurrent Neural Network) model and an ARX (Autoregressive Regressive) model with exogenous variables are combined to construct a guide deviation dynamic characteristic model to predict the guide deviation, the advantage that the GRU is good at processing a long-interval and long-delay time sequence is fully utilized, and the correlation characteristics before and after the shield guide deviation data time sequence are fully excavated; the model not only has the global nonlinear description capability, but also has the capability of describing the time sequence correlation characteristics of the system, the dynamic characteristics of the system are more comprehensively described, and the model is a nonlinear model which is more in line with the guiding actual working characteristics of the shield tunneling machine, and is higher in generalization capability and higher in precision.

The invention relates to an active and reactive collaborative optimization control method for a photovoltaic energy storage system based on a network-forming inverter, and the method achieves the effective control of active and reactive power through simulating the characteristics of a synchronous generator. And then collaborative optimization of active and reactive power control parameters is carried out. A system frequency response model is utilized to describe dynamic response of system frequency, and a dynamic equation of the system is converted into a discrete form in combination with the dynamic equation of the energy storage unit. Meanwhile, the optimization model is solved by considering system capacity limitation, so that transient frequency characteristics are improved, and frequency deviation after system disturbance is reduced. In addition, the minimum active power loss is taken as an optimization target, the voltage deviation is taken as a penalty term to be substituted into a target function, and a reactive power optimization target function is established. A PSO algorithm is adopted to solve a reactive power optimization function, and reactive power output of photovoltaic and energy storage is adjusted by adjusting VSG control parameters, so that the economical efficiency of a power grid is improved on the premise of ensuring qualified voltage.

The invention discloses an engine additive component service life prediction method based on modal polycondensation and expansion, and the method specifically comprises the steps: firstly, achieving the grid discretization of an initial defect structure based on a finite element method, and carrying out the local refinement of a defect region; performing modal polycondensation on the overall model to obtain a low-dimensional system model, and performing rapid calculation under the low-dimensional model to obtain modal response; establishing a conversion relation between a displacement vibration mode and a stress vibration mode, and obtaining full-field stress by utilizing modal response and based on modal extension; and solving a stress intensity factor-time history of the crack tip by adopting a stress extrapolation method, obtaining a delta K-R-n three-variable rain flow matrix by utilizing a rain flow counting method, and finally carrying out residual life evaluation through an iLAPS model. According to the method, system dynamics, fracture mechanics and modal order reduction technologies are fused, a new efficient and accurate fatigue life prediction path adapting to complex working conditions is provided for additive manufacturing components with defects, and the method has remarkable engineering application value and popularization potential.

The invention discloses a method and system for driving SOAR automatic disposal based on system dynamic risk assessment. According to the invention, a closed-loop protection system including data acquisition and preprocessing, dynamic risk assessment, disposal decision triggering, SOAR automatic disposal execution, system total risk value calculation, system-level risk determination, asset risk weighted upgrading, manual intervention triggering, manual disposal feedback and alarm level overall improvement is constructed; and accurate identification and efficient response of network security threats are realized. According to the method, the total risk value of the system is calculated through a multi-dimensional dynamic risk assessment model in combination with an asset value weight algorithm, and real-time upgrading of the asset risk level is realized through a risk threshold dynamic adjustment mechanism, so that a two-level response mechanism (automatic disposal-manual takeover) is realized, and the automation efficiency and the manual reliability are balanced. According to the invention, precise and efficient disposal of network security threats and intelligence of security protection are realized, and the method can be widely applied to core service systems of various industries.

The invention relates to the field of new energy system dynamic load shedding, in particular to a new energy system dynamic load shedding method based on real-time load analysis. Comprising the following steps: acquiring power grid real-time load data and new energy system output, and performing time domain analysis on the power grid real-time load data to obtain a load fluctuation rate and a load change acceleration; predicting and standardizing a load fluctuation rate at a future moment to obtain a normalized load characteristic parameter at the future moment; predicting the output of the new energy system at the future moment, and calculating the deviation between the output of the new energy system at the future moment and the load power; introducing a normalized load characteristic parameter at a future moment, calculating an initial output constraint factor, and carrying out amplitude limiting processing to obtain an output constraint factor; and the adjusted new energy system output is calculated through a dynamic load shedding adaptive adjustment algorithm. The technical problem that the analysis result is distorted due to the fact that low-frequency sampling is mostly adopted to obtain load data in the traditional technology and the rapid change characteristic of the load is difficult to capture is solved.

An adaptive impedance-based multi-mobile-robot collaborative transportation control method. Each mobile robot estimates the actual pose and ideal pose of a reference point and the first and second derivatives of the ideal pose by means of two finite-time fully-distributed observers, respectively; and then, on the basis of the estimated poses of the reference point, the pose of an end-effector of a mechanical arm, and closed-chain constraints for collaborative transportation, an ideal trajectory of the end-effector of the mobile robot, and an estimated value of a pose deviation between the end-effector of the mobile robot and the reference point are obtained. An adaptive impedance system of each mobile robot is interconnected with a virtual energy tank, and the energy tank is used to guide the updating of impedance parameters, thereby ensuring the passivity of the entire collaborative adaptive impedance system. To process unknown system dynamics of mobile robots, an asymptotic tracking adaptive neural network controller is designed using a neural network, thereby asymptotically achieving an ideal adaptive impedance relationship. The operational accuracy of multi-robot collaborative transportation systems is improved while ensuring safe collaboration.

The invention relates to an intelligent industrial control parameter self-tuning method and equipment. The method comprises the following steps: performing piecewise linearization processing on an actuator input and output characteristic curve in the industrial control system, constructing an upper-layer agent model and a lower-layer agent model, and combining to obtain a system dynamic characteristic prediction model; performing state observer design and gain matrix optimization on the industrial control system based on the system dynamic characteristic prediction model to obtain a system state observation vector and an interference compensation vector; inputting the system state observation vector and the interference compensation vector into a fixed time convergence controller for control parameter calculation to obtain a system control input matrix; and performing adaptive gradient descent operation and momentum item optimization on the system control input matrix, and outputting an optimal control parameter matrix in combination with the system dynamic characteristic prediction model. According to the invention, real-time automatic adjustment of the control parameters is realized, the steady-state error of the system is effectively reduced, and the control performance of the control system under different working conditions is ensured.

The invention belongs to the related technical field of mobile robots, and discloses a rolling-walking composite motion control method and device for a double-wheel-foot robot, and the method comprises the steps: (1) simplifying the rolling motion of wheels of the double-wheel-foot robot into the sliding motion of a sliding block; (2) constructing a system kinetic equation of the double-wheel-foot robot based on the simplified kinetic model; constructing a constraint equation based on the contact state of the two legs and the ground; discretizing the system kinetic equation, the cost function and the constraint equation, and converting model prediction control problems corresponding to the system kinetic equation, the cost function and the constraint equation into nonlinear problems for numerical solution so as to complete leg motion planning; (3) calculating the rotation speed of the wheel based on the leg motion planning result; and (4) based on the rotation speed of the wheels and the leg motion planning result, a whole-body control algorithm is adopted to calculate and obtain moment instructions of leg joints and the wheels. According to the invention, the state space dimension is reduced, and the wheel-ground contact constraint is simplified.

The invention provides a magnesium alloy electric drive main shell mold machining precision control method and system, and belongs to the field of precision numerical control machining. The method comprises the steps that dynamic milling force, key point temperature and position signals of all shafts are synchronously collected through a multi-source sensor; a recursive least square method is adopted to identify the dynamic rigidity and damping coefficient of the tool-workpiece system on line, and the thermal deformation drift distance is calculated in combination with a temperature signal; predicting a three-dimensional deformation error vector in real time based on the dynamic model, thermal deformation and geometric errors, and constructing a space compensation field; macro motion compensation is achieved by modifying numerical control program coordinate points, and meanwhile high-frequency micro-amplitude compensation is conducted through a spindle tail end fast tool servo system. And establishing an adaptive disturbance observer to perform online estimation on unmodeled disturbance and feed-forward compensation, and realizing online self-tuning of model parameters in combination with in-situ measurement feedback. Real-time sensing, full-band dynamic compensation and closed-loop self-adaptive control of multi-source errors are achieved, and the machining precision and long-term stability of the mold are remarkably improved.

The invention relates to the technical field of power system control, solves the problems of network attack, limited bandwidth and transmission delay of a power system in a signal transmission process and conservative DET parameter selection and controller design, and particularly relates to a power system dynamic event triggered load frequency control method under Dos attack and random delay. Comprising obtaining a continuous time system parameter matrix; setting an event triggering threshold function; state parameters facing Dos attack and random time delay conditions are set; constructing a state feedback controller; and collaborative design optimization is carried out by applying a GA optimization algorithm. Compared with a traditional control method, the method has the advantages that on the premise of ensuring the stability of the system, by optimizing event triggering conditions, the communication requirement and bandwidth occupation are remarkably reduced, the utilization efficiency of communication resources is improved, network congestion and delay possibly caused by frequent communication are reduced, and the service life of the system is prolonged. And the operation efficiency of the whole power system is improved.

The invention provides a solid waste recycling full life cycle environmental impact evaluation method, which belongs to the technical field of solid waste recycling, and evaluates environmental impact by constructing a system boundary and time dynamic list including solid waste collection, transportation, pretreatment, recycling and product use stages and applying a time weighted characterization model. A time discount rate is introduced to quantify future environmental influence, a multi-scene influence prediction function is adopted to construct a simulation matrix, and an environmental influence time distribution curve is calculated. A four-dimensional index system of resource recovery efficiency, energy substitution efficiency, pollutant emission reduction and environmental impact mitigation is established, a system dynamics and Envi rPred ictor deep learning model is combined to predict a solid waste long-term environmental behavior, an environmental impact comprehensive evaluation matrix is constructed, characterization factors are dynamically adjusted, an iterative optimization mechanism is formed, and the environmental impact mitigation efficiency is improved. Scientific evaluation on the solid waste recycling full life cycle environment influence is realized.

The invention provides a new energy automobile high-voltage system dynamic risk assessment method and device based on multi-source data fusion, and is applied to the technical field of data processing. According to the method, data such as high-voltage component operation parameters, battery states, environment perception, fault history and whole vehicle control instructions are obtained. Pre-processing the first working condition label, the second working condition label and the third working condition label, analyzing a CONTROLSIGNAL field to obtain a working mode of the high-voltage system, and generating a dynamic working condition label; combining a Pearson's correlation coefficient and XGBoost feature importance, screening risk sensitive features from the two, and forming a risk related feature subset; the method comprises the following steps of: establishing an improved Bayesian network model, training a subset by using an improved Bayesian network model, establishing an exclusive risk assessment model for different working conditions, reasoning real-time data by using the model to obtain a probability value of each risk dimension, and generating a dynamic risk assessment result and an early warning signal in combination with grade standard judgment.

The invention discloses a steering engine accurate control method and system based on an optimization control algorithm, and the system comprises a data collection module which collects key working parameters of a steering engine in real time through a plurality of sensors, and carries out the data processing, thereby forming a state vector; the self-adaptive filtering module is used for carrying out self-adaptive filtering fusion processing on the data acquired by the sensor and dynamically estimating the attitude angle and the corresponding angular velocity of the aircraft; the sliding mode control module constructs a sliding mode surface and a control rate of a steering engine system according to the dynamic error of the system, calculates control input of each axis of the aircraft according to the attitude angle real-time state error, and generates a sliding mode control signal; the driving execution module is used for converting the control signal into a voltage or PWM signal and inputting the voltage or PWM signal into a steering engine, so as to change thrust and realize adjustment and control of a posture or a position; and the real-time feedback module monitors the current state of the steering engine in real time to realize dynamic correction and real-time optimization. The adaptive filtering and sliding mode control method provided by the invention is higher in control precision, quicker in system response and higher in adaptability to complex environments, and can effectively improve the application performance and stability of a steering engine execution system in the fields of industrial automation, aerospace, intelligent manufacturing and the like.

The invention relates to the technical field of medical instruments, in particular to a medical instrument inventory prediction and optimization method and system based on big data, and the method comprises the steps: collecting hospital inventory, supply chain logistics, Internet of Things sensor and external environment data in real time, and generating a dynamic unified representation vector through cleaning, standardization and modal alignment; and inputting the vector into a hybrid modeling system, predicting future inventory requirements by using a deep neural network and calculating a confidence interval, simulating logistics and storage processes by using discrete events based on supply chain system dynamics, and realizing bidirectional feedback and adaptively updating model parameters through a Bayesian updating algorithm. And finally, the digital twin simulation platform performs discrete simulation on a future inventory operation scene, and generates a feedback signal for online updating the hybrid modeling system, so that closed-loop adaptive updating of inventory prediction and optimization is realized, and the accuracy and response speed of inventory management are remarkably improved.

The invention relates to the technical field of intelligent risk analysis, in particular to an intelligent risk analysis system of a financial system. The system comprises a data flow analysis module, a relational graph construction module, an abnormal mode detection module, a security boundary monitoring module, a staged strategy planning module, a scene simulation evaluation module, a decision support module and a comprehensive risk management module. A graph database and a graph convolutional network are utilized to deeply analyze a financial entity relationship, reveal a risk association network, improve abnormal behavior recognition through a K-mean value and an isolated forest algorithm, enhance security boundary monitoring through a random forest algorithm, enable risk threshold adjustment to be more dynamic, combine a decision tree with a genetic algorithm, optimize a risk management strategy, and improve risk management efficiency. The system dynamics and proxy model technology in scene simulation evaluation strengthens risk prediction and increases foresight, principal component analysis and risk matrix evaluation are applied in a comprehensive risk management module, a quantitative analysis tool is provided, and more systematic and comprehensive risk management is realized.

The invention discloses a task scheduling method and system based on big data analysis, and particularly relates to the technical field of big data analysis and intelligent scheduling, and the method comprises the steps: carrying out the modeling based on a graph structure and multi-scale time sequence features through a space-time prediction module, and precisely pre-judging resource demands and system dynamics; the man-machine cooperation module generates a scheduling strategy considering efficiency and constraint through dynamic weight balance AI calculation and artificial experience; the active scheduling module utilizes a health degree evaluation and fault propagation blocking mechanism to realize rapid isolation and resource migration of fragile nodes; the knowledge management module combines a knowledge graph and reinforcement learning, continuously optimizes a strategy library and digests rule conflicts. According to the method, resource prediction errors are remarkably reduced, task processing delay is shortened, fault recovery efficiency is improved, high reliability of key services is ensured, meanwhile, the method adapts to complex and changeable unforeseen scenes through self-learning ability, and the method can be widely applied to resource scheduling and fault-tolerant management in the fields of cloud computing, industrial Internet of Things and the like.

The invention provides a logarithmic terminal sliding mode control method for a nonlinear system, and belongs to the technical field of sliding mode control. According to the invention, a first-order logarithmic terminal sliding mode control strategy and a super-torsion logarithmic terminal sliding mode control strategy are obtained based on a designed logarithmic terminal sliding mode surface, a logarithmic terminal sliding mode cooperative control architecture oriented to a nonlinear system is obtained, multi-objective optimization of buffeting suppression, global rapid convergence and sliding time explicit is realized, and the method is suitable for large-scale popularization and application. The global convergence speed of the system state on the sliding mode surface can be improved while sliding mode control buffeting is weakened, the sliding time of the system state on the sliding mode surface is explicitly given, the strict mathematical correspondence between the controller gain coefficient and the system dynamic performance is determined through the explicit analytical expression of the sliding time parameter, and the system dynamic performance is improved. A quantitative theoretical basis is provided for parameter setting of an industrial field, and the system robustness under a complex working condition is remarkably enhanced.

The invention discloses an informatization system dynamic verification method based on a zero-trust architecture, and the method comprises the steps: collecting and standardizing user operation requests of all business systems, and achieving unified resource and organization identification; a distributed encryption operation log link is constructed through an end-to-end encryption protocol, and safe synchronization and tampering prevention of an operation behavior abstract are guaranteed; extracting a multi-dimensional right decision variable, inputting the multi-dimensional right decision variable into an adaptive artificial intelligence decision model, dynamically generating a reversible permission factor set, and supporting fine-grained and rollback permission authorization and real-time automatic revocation; the log chain is encrypted and recorded in the whole process, intelligent abnormity monitoring and end-to-end traceability are combined, the consistency and compliance auditing of the global permission state are supported, and the security, traceability and intelligent self-adaptive treatment capacity of sensitive operation in the multi-organization cooperation environment are effectively improved.

The invention provides a stochastic simulation method and device for an open quantum system, and the method comprises the steps: decomposing the total Hamiltonian of a new system into a linear term combination of a Fouli operator, and constructing a combination of quantum gates through the coefficient of the linear term combination. And then, operating a classical sampling method taking a Fouli operator as a sampling sample, constructing a quantum circuit by using the combination of quantum gates, and finally obtaining a mean value of the target observed quantity in the open quantum system according to an operation result of the quantum circuit. According to the method, a large-scale actual quantum system dynamics problem is simulated by utilizing a shallow quantum circuit, limitation of quantum chip resources is relieved, an error relieving technology is utilized, the circuit depth is reduced, a part of calculation processes are allocated to a classic computer, and processes related to observed quantity are only operated on the quantum computer, so that the calculation efficiency is improved. And the calculation results of the two parts are integrated on a classic computer, so that quantum hardware resources are saved.

The invention relates to the technical field of process optimization control, in particular to a single crystal furnace crystal pulling process control method and system based on an intelligent optimization algorithm. Identifying to obtain equipment operation parameters and process state variables; obtaining a state estimation value of the controlled medium based on the equipment operation parameter and the process state variable; determining target operation parameters of the controlled equipment according to the technological process and the equipment operation parameters; on the basis of the target operation parameters, expected changes of process state variables are estimated in combination with system dynamic response characteristics, and predicted state variables are obtained; calculating a medium distribution parameter according to the equipment operation parameter, the process state variable and the state estimation value; generating a state prediction value of the controlled medium based on the medium distribution parameter, the target operating parameter and the predicted state variable; and integrating the state estimation value and the state prediction value, and outputting a control instruction for adjusting the execution structure according to a preset adjustment rule. The closed-loop regulation of the controlled medium is realized through the control instruction.

The invention provides a process monitoring improvement method and device for a dynamic measurement function of a system, and the method comprises the steps: constructing a kernel mode dynamic measurement function assembly which comprises a strategy library and a reference library; when a measurement task is started, traversing all process lists in the system through a kernel mode dynamic measurement function component, and detecting whether a main execution file corresponding to a current process is in a measurement strategy list or not; traversing the vma list of the current process, checking whether each vma stores a reference value in a reference library or not, measuring the Hash value of the vma and comparing the Hash value with the reference value, if yes, continuing to traverse the next vma, and if not, making a decision according to a measurement strategy; and traversing the next process of the process list until the process list is traversed. According to the method, the strategy is issued for the process main body, and the whole process is used as a measurement target, so that the problems of measurement omission and incapability of proving the actual credibility of the monitored process are solved.

The invention discloses a semi-physical simulation platform for testing the safety of an unmanned surface vehicle, and relates to the field of simulation testing, and the platform comprises a shore-based monitoring system, a hardware simulation system, a dynamics simulation system, an unmanned surface vehicle scene editing system and an unmanned surface vehicle scene simulation system. And the dynamics simulation system is connected with the hardware simulation system. The unmanned ship scene simulation system is connected with the unmanned ship scene editing system, the dynamics simulation system and the hardware simulation system. Wherein the unmanned ship generates a control instruction based on control data and a task instruction input by the shore-based monitoring system; the hardware simulation system simulates the environment temperature and the marine environment stress of the safety test of the unmanned ship so as to obtain the pose data of the unmanned ship; the unmanned ship scene simulation system calls a scene configuration file based on the pose data to generate a simulation test virtual scene; and the unmanned ship adjusts the control instruction based on the simulation sensor data. According to the invention, the authenticity and credibility of the real installation test of the unmanned ship can be improved.