4192 results about "Dynamic control" patented technology

The present invention provides an energy storage synchronous coordinated management method and system based on virtual synchronization technology. The method comprises: selecting an appropriate energy storage device according to the requirements of a power grid and collecting related parameters; building a dynamic model of a virtual rotor, and setting rotational inertia of the rotor and torque parameters; building an adaptation layer; configuring a line monitoring apparatus, carrying out a safety operation when the fault occurs in the adaptation layer, or carrying out dynamic control and predictive planning in the absence of faults. The present invention utilizes intelligence to reduce reliance on experience, and enables dynamic optimization to improve the automation level of energy storage control.

The invention provides an intelligent factory data processing method and system based on the industrial Internet, and the method comprises the steps: firstly obtaining a real-time industrial data set collected by a multi-mode sensor in a target production region, covering various data, such as equipment vibration signals, carrying out the time sequence synchronization processing of the real-time industrial data set, and generating a synchronization data block; the method comprises the following steps of: extracting dynamic state characteristics of a production line from the data, calling a pre-trained anomaly detection model to carry out multi-dimensional anomaly detection, outputting an anomaly detection result, matching a preset expert knowledge base according to the anomaly detection result, and generating a dynamic control instruction set containing equipment adjustment parameters and the like; finally, production parameter configuration is adjusted based on the dynamic control instruction set and fed back to the industrial control terminal in real time, optimization of current production resource configuration is achieved, and the production efficiency and the resource utilization rate of an intelligent factory are effectively improved.

The invention provides an intelligent temperature adjusting method and system for an injection molding mold. The method comprises the steps that real-time multi-source sensor data flow is generated by collecting temperature data and pressure data of all areas of the injection molding mold; performing feature extraction on the real-time multi-source sensor data flow to obtain temperature field distribution features; a temperature field distribution prediction model is obtained through training based on the temperature field distribution characteristics and a differential private Bayesian optimization algorithm, and then a mold temperature prediction result is output through the model; performing feature fusion on the mold temperature prediction result and the pressure data, constructing input features of a neural network controller, training based on the input features and a positive definite curvature learning method to obtain the neural network controller, and generating a power control signal of a heater and a flow control instruction of a water cooling system through the neural network controller, and based on the control signal and the instruction, the temperature field of the mold is dynamically controlled through the adjustable clearance compensation mechanism. According to the invention, the precision and response speed of temperature adjustment are improved.

The present application relates to the field of wire laying control technology, and discloses a composite material automatic wire laying forming optimization method, system, medium and wire laying machine. The method comprises: inputting dynamic tension control data into a fuzzy neural network calculation engine to obtain a dynamic compensation model for the retraction and release speed; based on the dynamic compensation model for the retraction and release speed, using a reinforcement learning engine to calculate the shearing sequence of multiple bundles and generate an intelligent shearing instruction set; constructing a re-delivery trajectory mapping relationship based on the intelligent shearing instruction set, and forming a re-delivery path matrix after iterative calculation by a digital twin engine; establishing a temperature field distribution prediction model based on the re-delivery path matrix, and constructing a temperature field dynamic control sequence after finite element analysis and calculation; substituting the temperature field dynamic control sequence into a multi-objective constraint equation, and outputting a laying strategy data packet after deep reinforcement learning iterative optimization. The present application realizes the precise coordination of multiple process parameters such as tension control, speed compensation, shear control, and temperature control.

The invention relates to the technical field of mobile energy storage vehicles, and discloses an AI-based mobile energy storage vehicle running state monitoring and analysis system which comprises a main controller, an AI monitoring coprocessor and a state analysis coprocessor. The main controller calls a monitoring analysis instruction and sends the monitoring analysis instruction to the state analysis coprocessor; an AI monitoring coprocessor dispatches a state analysis instruction and processes multi-modal data; and the state analysis coprocessor analyzes the parameters to generate a reference monitoring signal, cooperatively monitors the battery temperature, the charging and discharging efficiency and the load fluctuation in real time, and generates an abnormal correction signal and a state control instruction in combination with an analysis optimization mode. The system realizes accurate analysis of multi-source data and dynamic control of the energy storage unit through data preprocessing, feature dimension reduction and deep neural network prediction, improves monitoring accuracy, dynamic adaptability and intelligent level, and ensures safe and efficient operation of the mobile energy storage vehicle.

The invention relates to the technical field of factory energy consumption management, in particular to a smart factory dynamic optimization management system based on digital twinning and big data analysis. Comprising a data acquisition and fusion module, a digital twinning construction module, a data analysis module, a dynamic optimization decision module and an anomaly diagnosis module. Constructing a digital twinborn model of a factory physical entity according to the collected data; constructing an energy consumption prediction model based on deep learning frameworks such as LTSM; when the energy consumption deviation exceeds the limit, abnormal root causes are positioned; and generating an energy consumption scheduling scheme based on a multi-objective optimization algorithm, and issuing an instruction to realize dynamic energy consumption adjustment. Through deep fusion of digital twinning and big data technologies, comprehensive and accurate simulation, multi-target collaborative optimization, rapid abnormality diagnosis and dynamic control of factory energy consumption are realized, the energy utilization efficiency is effectively improved, the cost is reduced, the intelligent level is improved, and the method has remarkable economic benefits and environmental benefits.

The invention relates to the technical field of energy storage control, in particular to a grid-connected type light storage and charging energy storage control system which comprises a power bearing capacity monitoring module, a light storage power adjusting module, a grid-connected power feedback module, a power grid stability margin calculation module and an energy storage power dynamic control module. According to the method, the voltage and current data of the grid-connected point are acquired in real time, the power transmission capacity is calculated in combination with the rated voltage range and the safety power upper limit, the power adaptation precision of the grid-connected point is improved, the photovoltaic power generation grid-connected proportion is dynamically optimized on the basis of comparison between load growth rate calculation and the power adjustment range, and photovoltaic power output curve change rate analysis is combined. Power output fine control, calculation of the load change rate and combination of power grid power factor data are achieved, a charging and discharging mode adjustment strategy is optimized, the frequency change rate and fluctuation degree are monitored, the stability margin of the power grid is enhanced, frequency deviation of the power grid caused by load fluctuation is avoided, and the new energy consumption rate and the operation reliability of the power grid are improved.

Provided in the present invention are an energy-storage synchronous and coordinated management method and system based on a virtual synchronization technique. The method comprises: selecting a suitable energy storage device on the basis of grid requirements, and collecting parameters of the energy storage device; on the basis of the parameters of the energy storage device, establishing a dynamic model of a virtual rotator, and setting a rotator rotational inertia and a torque parameter; establishing an adaption layer, so as to realize the interaction between the virtual rotator and a physical energy storage device; providing a line monitoring apparatus in a grid so as to collect real-time data of the energy storage device and detect, in real time, whether a fault occurs, and the adaption layer executing a safety operation when a fault occurs, and performing dynamic control and prediction planning when no fault occurs, so as to solve the problem of the uneven distribution of resources; and the adaption layer determining whether grid nodes and the real-time data of the energy storage device are normal, and if the grid nodes and the real-time data of the energy storage device are abnormal, performing detection and setting again, and if the grid nodes and the real-time data of the energy storage device are normal, saving a log and ending the process. In the present invention, an intelligent means is used to replace a conventional experiential control method, such that the dependence on experience is reduced, and dynamic optimization can be performed to increase the level of automation of energy storage control.

The invention relates to the technical field of automatic industrial control, and discloses an automatic industrial equipment plc data acquisition monitoring control method and system. The method comprises the following steps: receiving a multi-source PLC data stream, and processing the multi-source PLC data stream through a multi-modal data fusion model to generate an equipment health degree evaluation matrix; constructing a dynamic control strategy generation model, and outputting a multi-target optimization instruction set; simulating the interaction influence of the equipment group by utilizing a process chain collaborative constraint model, and optimizing real-time control parameters of an instruction set; parameters are adjusted in parallel through a distributed edge computing framework, and an equipment cooperative control signal is output to an industrial monitoring center. The system comprises a distributed data acquisition module, a multi-modal fusion calculation module, a dynamic strategy generation module, an edge cooperative control module and an industrial communication gateway. According to the invention, multi-source data can be efficiently processed, equipment health can be accurately evaluated, a control strategy is optimized, equipment collaboration is realized, and industrial production efficiency and energy utilization rate are improved.

The invention relates to the technical field of liquid cooling servers, and discloses a liquid cooling server safety management system and method, and the method employs a multi-mode sensor network to synchronously collect the thermodynamic parameters of a liquid cooling system at 100 Hz, and constructs a three-dimensional thermal field digital twinborn model after the processing of an extended Kalman filtering algorithm. A distributed cooling strategy is designed based on a federated learning framework, and each node locally trains a thermal dynamic prediction model and is optimized by a central aggregator. A dynamic control instruction is generated by using a near-end strategy optimization algorithm, and cooling liquid flow distribution is optimized in combination with a quantum derivative simulated annealing algorithm. And designing a dual-threshold phase change control mechanism, establishing a block chain log to ensure traceability and tamper resistance of the instruction, and realizing closed-loop feedback control through a CAN bus. The system and the method can accurately monitor and intelligently control the liquid cooling system, improve the heat dissipation efficiency, reduce the power consumption, and guarantee the data safety and the system stability.

The invention relates to an electric energy comprehensive management control method for high and low load working conditions, and belongs to the field of energy supply system control. The method comprises the following steps: acquiring current operation information of an energy supply system, including a working condition mode, a target power demand and a battery state of charge (SOC) value, and further judging a working condition type and calculating a total power demand value of the system; on the basis, a power distribution scheme of a power generation unit and a battery is judged according to an SOC value and a power demand: under a normal working condition, the balance of power generation and battery charging and discharging is realized by setting an SOC threshold interval, and under an emergency working condition, the maximum rate discharging of the battery is dynamically controlled or the charging current is limited to cope with load fluctuation. According to the system, the bus voltage and the SOC are maintained in a safe range through a closed-loop regulation mode, so that the stability and the response capability of the energy supply system are improved.

The invention discloses an electric vehicle parking lot fire prevention and control system based on multi-source information fusion. A sensing layer captures the environment state of an electric vehicle parking lot and the operation parameters of a vehicle battery in real time through a multi-mode sensor network; the network layer adopts an intelligent fusion network architecture to realize communication between the sensing layer and the data processing layer; the data processing layer is used for performing fusion analysis on the environment state and the operation parameters, judging a fire risk level and generating a dynamic control instruction; the data processing layer is composed of a multi-dimensional analysis engine, and a hierarchical progressive intelligent analysis architecture is adopted to realize multi-level research and judgment of fire risks; and the application layer receives the dynamic control instruction generated by the data processing layer, and realizes full-process closed-loop management of fire prevention and control through a multi-mode terminal device. The method not only improves the fire prevention and control precision of the electric vehicle parking lot, but also has good interpretability, and can be directly applied to a fire prevention and control system of the electric vehicle parking lot.

The invention relates to a subway construction progress dynamic regulation and control method and system based on digital twinning. According to the method, a digital twin model is constructed by collecting multi-source heterogeneous data of a construction site, and a multi-dimensional construction progress characterization model is generated. Based on the model, progress deviation calculation and risk prediction are carried out to obtain a current progress deviation value. And once the value exceeds a preset threshold value, generating a regulation and control scheme set by using a multi-objective optimization algorithm. Then, the schemes are simulated and verified in a digital twinning environment, and a feasible regulation and control scheme set is formed to guide actual construction progress regulation and control. According to the method, by means of the digital twinborn technology and multi-source data fusion, the construction state is presented in real time, progress deviation and risks are rapidly recognized, an effective regulation and control strategy is formulated through an optimization algorithm, the construction progress management efficiency and accuracy are improved, the capability of responding to environment changes and emergencies is enhanced, construction delay and resource waste are reduced, and the construction progress management efficiency and accuracy are improved. And the subway construction management level and quality are optimized.

The invention relates to the technical field of unmanned aerial vehicle dynamic dust suppression intelligent decision making based on multi-sensor data fusion, in particular to a loess photovoltaic construction dust suppression unmanned aerial vehicle spraying dynamic regulation and control device and method, and the method comprises the steps: building a dynamically updated four-dimensional concentration field through a sensor cooperation unit in combination with a turbulence diffusion model of a dust field reconstruction engine; and the strategy knowledge base is optimized to shorten the flight path planning decision-making period from the minute level of the existing offline planning to the second level response. And the anti-interference execution unit realizes accurate spray trajectory tracking under a complex wind field condition. And a real-time evaluation closed loop of the dust suppression effect is constructed by a dual-spectrum imaging and deep learning analysis technology of the efficiency feedback unit, so that a dust field model can dynamically correct boundary condition parameters. The response lag time of an existing dust suppression system is shortened, meanwhile, the spray coverage rate is increased, energy consumption is reduced on the premise that the dust suppression effect is guaranteed, and an intelligent solution is provided for photovoltaic construction flying dust treatment.

The invention discloses an intelligent control method and system for AR equipment, and the method specifically comprises the steps: determining a pupil center position coordinate and a cornea reflection point position coordinate of a user according to an eye image of the user, and inputting the pupil center position coordinate and the cornea reflection point position coordinate into a geometric mapping model for matching, obtaining user sight direction information; acquiring image brightness value distribution of an area near a user fixation point, determining a plurality of candidate saliency areas according to the image brightness value distribution, and comparing the candidate saliency areas with a preset saliency area template to determine a user attention focus; and acquiring head posture information of the user in real time by adopting a head posture tracking algorithm, and dynamically controlling the shooting direction of a camera of the AR equipment according to the head posture information, the sight direction information of the user and the attention focus of the user. According to the method and the device, accurate alignment of the AR content and the visual focus of the user is realized, and the immersion and interaction experience of the AR application are greatly improved.

The invention discloses a BMS-based multifunctional integrated monitoring and fire control method and system, and the method comprises the steps: synchronously collecting the temperature, combustible gas concentration and dynamic environment monitoring data in a container through a BMU battery box management unit of a BMS, and generating a space-time correlated container environment state matrix; based on the environment state matrix, extracting temperature anomaly features, gas concentration gradient features and dynamic environment fluctuation features through a multi-modal feature fusion model, and constructing a multi-dimensional risk feature vector; inputting the multi-dimensional risk feature vector into a fire-fighting early warning optimization algorithm to generate a graded fire-fighting early warning signal; and according to the graded fire-fighting early warning signal, an active fire-fighting intervention strategy is triggered through a BMU unit of the BMS, a fire extinguishing device, a ventilation system and a power cut-off module are dynamically controlled, and a fire-fighting response path is synchronously optimized to be matched with the current risk grade. According to the embodiment of the invention, graded fire-fighting early warning can be realized, and the accuracy and timeliness of fire early warning are improved.

The invention discloses an intelligent discharge port dynamic regulation and control system and method based on multi-source time sequence fusion, and relates to the technical field of intelligent water affairs, and the system comprises a data collection module which is used for obtaining the multi-source data of a target area in real time; the space-time fusion module is used for mapping the meteorological radar data to the topological space of the drainage pipe network through a space-time alignment encoder, dynamically fusing the meteorological radar data, the surface runoff sensor data and the historical drainage data by adopting a multi-head attention mechanism, and generating a space-time fusion tensor; the prediction model module is used for constructing a TCN-LSTM hybrid model, taking the space-time fusion tensor as input and taking future drainage flow as output; and the dynamic regulation and control module is used for optimizing the gate opening degree and the pump station power control sequence in a rolling manner based on a model predictive control algorithm, and performing dual-target optimization by combining real-time electricity price data and overflow risks. According to the system, the prediction precision of the drainage flow can be improved, and double-target balance of the overflow risk and the energy consumption cost can be achieved.

The invention discloses an indoor temperature real-time regulation and control method of a heat distribution pipeline and a control system of the indoor temperature real-time regulation and control method, relates to the technical field of dynamic control of a heat distribution pipe network, and solves the problems of hydraulic oscillation and temperature control hysteresis caused by the fact that local valve regulation neglects whole-network coupling and a first-order linear model is difficult to describe multi-order thermal inertia and large heat capacity in the prior art. According to the scheme, on the basis of pipe network distributed PDE / lumped parameter hybrid modeling and in combination with extended Kalman filtering and unscented Kalman filtering on-line identification, a feedforward decoupling compensation item is generated through spectral decomposition, a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed, a control instruction is issued according to a pump-first and valve-second serialization strategy, and a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed. Meanwhile, the model weight and the prediction time domain are dynamically adjusted; according to the method, the global balance capability and the temperature tracking precision of heat distribution pipeline regulation and control are remarkably improved.

The invention relates to the technical field of water pump energy-saving control, and discloses a water pump energy-saving control system based on an intelligent data model, which comprises a data sensing module, a modal analysis module and the like. The data sensing module collects flow, lift and power data and is provided with a dynamic monitoring window; the modal analysis module divides operation modes and generates an energy efficiency characteristic matrix; the rule construction module extracts a core energy efficiency factor and constructs an energy-saving regulation and control strategy; the priority scheduling module analyzes the control priority matrix and evaluates an energy efficiency deviation index; a mapping calibration module fits the optimal energy efficiency reference curve to generate an energy efficiency deviation gradient map; the control execution module generates a regulation and control instruction set according to the map. The system realizes fine management and energy efficiency optimization of water pump operation through multi-mode energy efficiency analysis and dynamic regulation, solves the problems of poor adaptability of traditional control working conditions, low energy-saving efficiency and the like, is suitable for water pump energy-saving control in the fields of industry, municipal administration and the like, and is accurate and efficient.

The invention relates to the technical field of computer control, and discloses an automatic computer control method and system based on artificial intelligence. The method comprises the following steps: firstly, acquiring multi-source heterogeneous sensor data, including environmental parameters, equipment running states and operation instruction logs; then, an environment state evolution model is constructed, an equipment health degree evaluation index is generated, and an operation instruction log is analyzed; and then, inputting the results into a multi-modal decision model to generate a dynamic control instruction set, constructing a control strategy topological graph through an adaptive optimization algorithm, and outputting an equipment regulation and control scheme. The system correspondingly comprises a multi-source data acquisition module, an environment modeling module, a health assessment module and the like. Multi-source data can be effectively processed, the health condition of equipment can be accurately evaluated, a control instruction and a regulation and control scheme can be intelligently generated, the intelligent level of computer control is improved, and the reliability and the operation efficiency of the system are improved.

The invention discloses a dynamic control method of a lithium battery module intelligent equalization management system, and relates to the field of lithium battery module equalization, and the method comprises the steps: constructing a hierarchical data fusion platform, and generating a battery state data set containing SOC, SOH and an inconsistency evaluation value; a credibility reasoning algorithm is adopted to calculate a single body imbalance credibility factor value to divide an equalization stage, and equalization current is dynamically adjusted; a charge and discharge curve health factor is extracted through an LAB health factor algorithm, SOH is updated online in combination with extended Kalman filtering, and an SOH attenuation rate is fed back to the battery model; in-module equalization is carried out through a bidirectional energy transfer circuit, inter-module equalization is carried out through a ring bus topology, and layered topology linkage control is formed. The method has the advantages that the consistency and the heat management efficiency of the battery pack are improved through the hybrid equalization strategy and the layered topology linkage control, the service life of the battery pack is prolonged, and the overall performance and the safety of the electric vehicle are improved.

The invention relates to the technical field of printed circuit board manufacturing and quality detection, in particular to a PCB forming detection method based on a hole site scatter diagram and SPC analysis, and the method comprises the steps: collecting the actual hole site data of a PCB substrate through an optical scanner or a laser measurement device, generating a scatter diagram containing coordinates, aperture and tolerance, and carrying out the detection of the hole site scatter diagram; an abnormal region is identified by using an improved density clustering algorithm; performing multi-dimensional analysis on the scatter diagram based on an SPC model, extracting characteristic parameters such as hole site density deviation, constructing a dynamic control limit, and calculating a CPK to judge a processing state; and when the CPK is lower than a threshold value, optimization instructions such as drilling path correction and drill bit wear compensation are generated and fed back to the equipment through the anti-saturation fuzzy PID controller to realize closed-loop control. According to the method, the hole site machining quality can be accurately detected, the machining parameters are adaptively adjusted, and the PCB forming precision and the production stability are effectively improved.

The invention relates to the technical field of intelligent vehicle control, in particular to a loader vehicle control method and system, computer equipment and a storage medium. The method comprises the following steps: collecting multi-modal data of the loader, wherein the multi-modal data comprises working environment data, dynamic load state data and vehicle attitude data of the loader; constructing a three-dimensional operation scene digital twinborn model based on the multi-modal data, and predicting vehicle stability influence data of the influence of the load change on the vehicle stability based on the dynamic load state data; based on the digital twinborn model of the three-dimensional operation scene and the vehicle stability influence data, a dynamic control strategy is generated by utilizing a reinforcement learning algorithm, and cooperative actions of steering, lifting and walking mechanisms are synchronously optimized; according to operation environment data and a dynamic control strategy, hydraulic system pressure and motor torque distribution are adjusted in a self-adaptive mode, and operation efficiency and safety are ensured.

The invention discloses a cooperative control method for multiple AGVs for cooperatively carrying heavy objects, and belongs to the technical field of automatic logistics, and the method comprises the steps: taking a high-dimensional environment description vector as a model condition, inputting the model condition into an improved conditional diffusion model which is introduced into a linkage constraint feature channel in a reverse denoising stage of a conventional conditional diffusion model, and obtaining a preliminary scheme; and then correcting the preliminary scheme by adopting a popular theory and a high-dimensional differential geometric constraint, and then ensuring cooperative control of multiple AGVs by adopting a dynamic environment perception and linkage state prediction mechanism, an online feedback and local path adjustment mechanism and a multi-heavy-load synchronous control strategy in control operation according to the corrected scheme. According to the method, the initial path is obtained based on the improved conditional diffusion model, the feasibility and the collaboration of a generation result are improved, the vehicle speed, the acceleration and the steering angle are controlled and optimized by applying a high-dimensional differential geometry dynamic control method, and the dynamic consistency of synchronous movement of multiple vehicles is ensured.

The invention belongs to the technical field of radial forging machine control, and particularly relates to a high-precision radial forging machine control system and a control method thereof.The method comprises the steps that a technological parameter database is established, dynamic parameters in the forging process are synchronously collected through a multi-source heterogeneous sensor, and workpiece surface temperature field distribution is obtained through an infrared thermal imager; measuring radial deformation through a laser displacement sensor group, and capturing internal defect characteristics of the material by using an acoustic emission sensor; the optimal forging frequency is calculated according to real-time material rheological characteristics, and a model predictive control algorithm is adopted to generate hammerhead motion trail correction; dynamic compensation control is implemented, and the back pressure of the hydraulic system is adjusted through strain rate feedback; and closed-loop quality evaluation is executed, online metallographic analysis is carried out in the forging intermittent period, process parameters are reversely corrected according to the grain size detection result, the fatigue life of the workpiece is predicted based on a digital twin model, and the effects that the whole forging process is dynamically controlled in real time, errors and deviation are effectively eliminated, and the forging precision is improved are achieved.

The invention discloses an AI-based method and system for optimizing and supervising an internal control process of an administrative institution, and relates to the technical field of intelligent management. Time sequence message transmission coding is carried out on the benchmark process and the actual process to achieve deep semantic alignment of the benchmark path and the actual path, the process execution semantic offset coefficient between the benchmark process and the actual process after semantic alignment is further quantified, and the process execution semantic offset coefficient is compared with a preset threshold value. Determining whether a path offset early warning prompt is generated or not; according to the method and the device, the process deviation can be captured in real time, early warning can be triggered, the real-time performance and objectivity of supervision are enhanced, abnormal risks in process execution are effectively prevented through an intelligent early warning mechanism, and the dynamic management and control capability with flexibility and normativity is provided for administrative institutions.

The invention relates to the technical field of manufacturing process control, and discloses a multi-dimensional process data co-simulation control method and system and a storage medium. The method comprises the following steps: firstly, collecting a multi-dimensional process parameter set of a plurality of process equipment in a manufacturing production line, carrying out cross-dimensional feature extraction, and generating a comprehensive feature matrix containing a time sequence feature, a spatial distribution feature and an energy consumption feature; according to feature relevance of different dimensions in the comprehensive feature matrix, a process parameter dynamic coupling model is constructed, co-simulation is carried out on interaction between process equipment, and a process state evolution sequence is output; extracting abnormal fluctuation characteristics in the sequence, and generating a process parameter adjustment instruction set; and finally, according to the adjustment instruction set and real-time process feedback data, dynamically correcting model simulation parameters, generating an optimized process control strategy, and executing the optimized process control strategy. According to the method, collaborative analysis and dynamic control of multi-dimensional process parameters are realized, and the accuracy and adaptability of process control are improved.

The invention belongs to the technical field of automatic detection, and discloses an online laser detection and grading regulation and control system for particle size distribution of crushed raw ore. The invention aims to solve the problems of halo effect interference caused by adhesion of fine dust in a complex industrial environment and insufficient detection precision of a traditional method. The system obtains dynamic scattering light field data through the laser scanning module, identifies a halo effect region and generates a compensation coefficient by using the light field preprocessing module, and realizes accurate reconstruction of particle boundaries in combination with the edge reconstruction module. The particle size distribution analysis module extracts multi-dimensional geometric features based on the accurate boundary data, and generates a real-time particle size distribution curve. The grading strategy generation module formulates a regulation and control strategy according to deviation characteristics of the curve and a preset standard, and the dynamic regulation and control module adjusts parameters of crushing equipment in real time and optimizes particle size distribution. The method effectively overcomes the halo effect interference, improves the detection precision and the system stability, breaks through the single limitation of a traditional method, and provides comprehensive data support for a downstream process.

The invention provides an automobile magnesium alloy pre-twin crystal deformation data analysis system, and relates to the technical field of data processing, and the system comprises a twin crystal correction module which is provided with a first target monitoring point and a second target monitoring point in a pre-twin crystal area, the first target monitoring point is located at a crystal boundary intersection, and the second target monitoring point is located at the center of a twin crystal zone; acquiring orientation difference angle and strain energy density difference data of the two monitoring points in real time, calculating a covariance matrix of change characteristics of the two monitoring points, and correcting a proliferation rate parameter and an orientation rotation parameter in the dynamic evolution matrix according to a correlation coefficient of the covariance matrix to obtain a corrected dynamic evolution matrix; and the optimization module is used for adjusting a hot extrusion process parameter group according to the twin crystal density gradient distribution output by the corrected dynamic evolution matrix, the parameter group comprises a mold temperature, an extrusion speed and a strain path, and the twin crystal density is dynamically controlled in a target interval. According to the invention, the complex stress state of the automobile part under the actual working condition can be accurately simulated.

Various systems and methods are directed to an electro-mechanical dice shaker gaming system designed to enhance fairness, transparency, and security in wager-based gaming environments. The system includes an electro-mechanical random number generator (RNG) assembly that physically isolates the dice shaking mechanism from the player terminal, preventing external interference from affecting game outcomes. The dice shaker mechanism is housed in a transparent enclosure, allowing players and casino operators to visually verify each roll. Integrated sensors, including tilt and vibration detectors, monitor environmental conditions to detect and prevent tampering. A camera-based monitoring system captures images of each dice roll, facilitating automated outcome verification and compliance auditing. The system supports modular configurations for multi-player gaming, real-time streaming for remote participation, and AI-driven fraud detection. Additionally, an adjustable mirror enhances dice visibility, and automated mechanisms allow dynamic control over the number of dice in play, offering a scalable and secure gaming solution.