2502 results about "Execution unit" patented technology

The invention relates to the technical field of industrial automation control, and discloses a change gear assembly processing technological parameter intelligent optimization system, which comprises a multi-dimensional data synchronous acquisition unit, an angular domain static map construction unit, a phase lead constraint control unit and an angle trigger execution unit, according to the method, a static angle-load topological graph with a main shaft angle as an index is established, a phase lead angle is calculated by combining inherent physical response lag time of a servo system, and a parameter adjustment instruction sequence is directly injected into a servo current loop at a preset angle window before physical load impact occurs; according to the feed-forward control mechanism based on angular domain reconstruction and phase lead constraint, the problem of physical lag of traditional time domain feedback control in response to high-frequency intermittent cutting is avoided, and zero time difference of periodic load impact and even pre-judgment torque compensation are achieved.

The invention relates to the technical field of artificial intelligence, in particular to a real-time anti-fraud monitoring system and method based on behavior reasoning and sentiment analysis, and the system comprises a multi-modal data collection unit, an edge preprocessing unit, a feature fusion and behavior reasoning unit, a large language model context reasoning unit, a risk assessment and decision unit, and an intervention execution unit. A log recording and federal incremental learning unit; the method has the beneficial effects that the traditional isolated single-mode detection is evolved into an emotion and behavior dual-channel collaborative multi-mode recognition system through millisecond-level coaxial alignment of voice, video and user operation logs; the robustness of dialect, noise and expression shielding is greatly improved through the multi-modal fusion model, so that the cross-scene recognition accuracy is improved by nearly three percent compared with that of a traditional single-voice scheme, and high-sensitivity capture of hidden and emotion control type fraud is truly achieved.

The invention discloses an electrical work ticket intelligent generation method and device, a medium and equipment in the technical field of power systems, and aims to solve the problems of serious module decoupling, dependence on manual connection and insufficient intelligent generation capability in the prior art. The method comprises the following steps: generating a required digital knowledge base and an electrical work task description generation work ticket based on a pre-constructed work ticket by utilizing a multi-model collaborative generation architecture; the multi-model collaborative generation architecture comprises a rule driving module used for calling a corresponding template based on task element matching; the task execution unit is used for executing a specific subtask according to a pre-deployed lightweight model or rule engine; and the large language model is used for performing semantic understanding and reasoning according to the electrical work task description, calling the rule driving module and the task execution unit according to a semantic understanding and reasoning result, and generating a work ticket in combination with a digital knowledge base required by work ticket generation. The method has both the accuracy of structured knowledge recognition and the flexibility of natural language generation.

The invention relates to the technical field of intelligent war game deduction, in particular to an intelligent war game deduction simulation system based on a digital twin fusion large model. Comprising a digital twin modeling unit which adopts a dynamic precision adaptive modeling mechanism; a large model decision unit; a deduction execution unit; and a dynamic interaction unit. A dynamic precision self-adaptive modeling mechanism is adopted by the digital twin modeling unit, the model precision can be dynamically switched on demand, the detail simulation demand and the computing resource consumption are balanced, and meanwhile, the sub-second-level dynamic synchronization of a virtual model and a war game deduction scene is realized through a time sequence calibration algorithm; and space confrontation, time sequence decision and rule triggering features are fused through the multi-modal feature analysis module, and the Wargame rule verification module is combined to embed entity performance boundaries and scene rule constraints, so that compliance confrontation decision parameters conforming to Wargame deduction logic can be generated, and the problem of insufficient model precision adaptation and decision constraints is solved.

The invention discloses an asynchronous parallel reasoning method, system and equipment for a hybrid expert model and a medium, which are corresponding schemes: decoupling synchronization of calculation and communication between GPUs (Graphics Processing Unit) caused by all-to-all set communication in expert parallelism, allowing asynchronous parallelism of model calculation and lexical metadata communication, and solving the problem of asynchronous parallelism of the model calculation and lexical metadata communication. Data communication overhead caused by expert parallelization is fully masked, and synchronization waiting overhead is eliminated; aiming at the phenomenon of uneven cold and heat of experts in reasoning, the hot experts are preferentially placed in the GPU, the cold experts are laterally loaded in the CPU so as to release the video memory space of the GPU, and the calculation efficiency of the GPU can be improved by increasing the batch size during reasoning; efficient resource scheduling is realized by dynamically selecting a computing unit which is most suitable for execution and a cold expert which needs to be loaded; generally speaking, the communication overhead and the waiting overhead during parallel reasoning of experts can be remarkably reduced, meanwhile, the calculation efficiency of the GPU is improved, and the overall throughput performance in the reasoning process is optimized.

The invention discloses a thermal balance control method for a liquid cooling charging module, and relates to the technical field of power electronics, and the method comprises the steps: deploying a multi-dimensional distributed temperature sensing array in a key heating region of the liquid cooling charging module; original data are acquired through the temperature acquisition module and transmitted to the temperature field reconstruction module, a three-dimensional dynamic temperature field model is constructed by using a spatial interpolation algorithm, and temperature values, temperature gradients and temperature difference distribution of all regions are output in real time; the thermal balance control module compares the model output data with a preset temperature threshold value and a temperature difference threshold value, and if the threshold value exceeding condition exists, a regulation and control instruction is sent to a partition cooling execution unit; the subarea cooling execution unit comprises three independent branches (corresponding to key areas), and each branch realizes accurate adjustment through an independent electromagnetic flow valve and a variable frequency water pump; and the steps are circulated until the temperatures of all the key areas reach the standard and the temperature difference of the adjacent areas meets the standard, so that the heat balance precision and the operation safety of the liquid cooling charging module are effectively improved.

The invention discloses a new energy automobile hybrid power system cooperative control device and method.The new energy automobile hybrid power system cooperative control device comprises a multi-source data collection module, a cooperative control center and an execution unit, and the multi-source data collection module transmits collected information data to the cooperative control center; the cooperative control center comprises a data fusion unit, an energy management module, a heat management module and a cooperative decision-making unit, the data fusion unit receives data information of the multi-source data acquisition module, carries out fusion processing, and transmits the data information to the energy management module and the heat management module; the energy management module and the heat management module output a control strategy to the collaborative decision-making unit, and the collaborative decision-making unit judges a correction strategy and then sends a control instruction to the execution unit. The method has the advantages that cooperative control of energy management and heat management is achieved, various dimension data of road conditions, driving behaviors, power states and heat source states are fused, the problem of system unbalance caused by single module optimization is solved, and dynamic cooperation of energy management and heat management is achieved.

The invention relates to the technical field of automobile brake control, in particular to a self-adaptive brake kinetic energy recovery control method and system based on multi-sensor fusion. Tire wear and road surface feature data are collected through a multi-source sensing fusion unit, and a tire wear coefficient and a road surface friction coefficient are generated through an intelligent decision calculation unit; the method comprises the following steps of: integrating the nonlinear correlation of the multi-source sensing fusion unit and the self-adaptive control execution unit through a rule and data fusion algorithm, outputting a comprehensive friction coefficient, and dynamically adjusting the braking kinetic energy recovery force and response time by the self-adaptive control execution unit according to the comprehensive friction coefficient. The system comprises a multi-source sensing fusion unit, an intelligent decision calculation unit, the self-adaptive control execution unit and a closed-loop feedback calibration unit. The closed-loop unit corrects data deviation through cross validation of the laser radar and the motor torque inversion model, the problems of poor working condition adaptation and unreliable data are solved, and the kinetic energy recovery efficiency, the braking safety and the driving smoothness are improved.

The invention relates to a source-containing power distribution network multi-target collaborative voltage optimization control method based on an FACMAC algorithm, and belongs to the technical field of photovoltaic inversion control. According to the technical scheme, a power distribution network physical system is composed of a plurality of feeder lines, a transformer, a line and a plurality of grid-connected photovoltaic inverters, and each inverter can measure operation information such as local voltage and current in real time; the data acquisition and communication system is used for acquiring node operation data and realizing low-delay communication; the multi-agent reinforcement learning control system is composed of a plurality of distributed agents and factorization centralized Critic modules, and whole-network voltage optimization decision can be carried out in training and execution stages. And the execution unit adjusts the reactive power output of the inverter in real time according to the control instruction. According to the method, the whole-network cooperative regulation and control capability is improved, the training efficiency bottleneck in a high-dimensional scene is relieved, the expression capability on a complex nonlinear coupling relationship is enhanced, and efficient, stable and extensible power distribution network voltage optimization control is realized.

A system for real-time detection and mitigation of morphing malware in high-density edge networks, consisting of: a data acquisition unit configured to receive, normalize, and encode multimodal telemetry data streams originating from at least one of the following domains: network traffic, process behavior, system call sequences, binary instruction traces, and control flow graphs; the data acquisition unit is further configured to compute feature embeddings over sliding time windows and apply privacy-preserving redactions prior to storage; a generative neural processor that is operationally coupled to the data acquisition unit and configured to generate synthetic morphing malware variants by learning probabilistic transformations of previously observed malicious data representations, maintaining semantic functionality while varying structural and behavioral features; a discriminative neural processor trained adversarially with the generative neural processor, wherein the discriminative neural processor is configured to detect morphing malware by evaluating a probability distribution over multimodal telemetry embeddings and classifying anomalous process and flow behaviors in real time; a coordination processor that is communicatively connected to both the generative neural processor and the discriminative neural processor and is configured to orchestrate adversarial co-training, regulate detection thresholds, calculate reinforcement-based penalties for false negative results, and trigger countermeasures as soon as a detection confidence level exceeds a predefined adaptive threshold; a secure, system-integrated inference and enforcement unit configured to perform low-latency countermeasures at the network edge, including selective packet filtering, flow isolation, process interruption, or system microsegmentation, based on instructions from the coordinating processor; and a hardware-embedded security enclave that is embedded in the system and configured to store cryptographic keys, neural model parameters, and integrity affirmation data to ensure the confidentiality, authenticity, and immutability of model artifacts and policy configurations.

The invention relates to the technical field of data disaster recovery, and provides a disaster recovery drill and disaster switching method and system, and the method comprises the steps: automatically recognizing multi-source heterogeneous database assets through a rule engine and a machine learning algorithm, and constructing a disaster recovery topological graph; a disaster recovery strategy is generated and optimized by adopting an intelligent algorithm based on the disaster recovery topological graph, and the disaster recovery strategy is dynamically adjusted in combination with real-time service requirements; fusing the historical disaster recovery data and the real-time monitoring data to reconstruct a disaster recovery strategy; executing data are collected in real time in the drilling process, and a disaster recovery strategy is optimized through a closed-loop feedback mechanism; monitoring a whole process state through a visual interface and displaying key indexes; based on the multi-dimensional evaluation model, quantitatively analyzing the drilling result, and determining a target disaster recovery strategy; and a security verification mechanism is adopted to authorize the key operation, a disaster recovery scene scheme is matched according to the fault type, and an automatic execution unit is controlled to complete disaster switching based on the target disaster recovery strategy. According to the invention, the disaster recovery switching accuracy in a complex scene is improved.

The invention relates to the technical field of intelligent regulation and control of fusion data, in particular to a rolled finished product-based size precision intelligent regulation and control system and method, which comprises a data acquisition unit, a data fusion unit, a deep learning prediction unit and a compensation execution unit, multi-source data such as temperature gradient, roll gap fluctuation and tension spectrum are collected in real time through a sensor array, thermal expansion compensation correction is carried out through a data fusion unit based on a rolling elasticoplastic deformation equation, an anti-interference coupling feature vector is generated by using adaptive Kalman filtering, and a deep learning prediction unit is used for predicting the rolling elasticoplastic deformation through a dual-channel space-time convolutional network. The thickness error and the width error are predicted by combining the constraint of a rolling force balance equation, the compensation execution unit dynamically generates the roll gap adjusting amount and the rolling force compensation amount according to a three-level response mechanism, phase lag compensation current is injected to suppress resonance, the high-end steel yield and the rolling precision are improved, and the rolling quality is improved. And an intelligent closed-loop control scheme is provided for high-precision rolling under complex working conditions.

The invention relates to the technical field of operation and maintenance safety compliance, and discloses an operation and maintenance service automation safety compliance detection system and method. The system comprises an operation and maintenance event acquisition unit, a strategy execution unit, a compliance analysis unit, a strategy optimization unit and a violation tracing unit. The operation and maintenance event acquisition unit captures operation and maintenance operation events and execution environment parameters in real time, packages the operation and maintenance operation events and the execution environment parameters into event data packets and transmits the event data packets to the strategy execution unit; the strategy execution unit extracts a reference strategy rule from the security strategy library, performs matching verification on the event and generates a result; the compliance analysis unit analyzes compliance fluctuation characteristics through a multi-dimensional compliance deviation model in combination with historical records; the strategy optimization unit dynamically adjusts the event capture frequency and corrects a strategy matching rule according to the fluctuation characteristics; the violation tracing unit counts the risk cumulant and generates a violation tracing instruction when the risk cumulant exceeds a preset capacity. According to the system, automation and dynamic adjustment of operation and maintenance safety compliance detection are realized, and a risk accumulation process can be effectively tracked.

The invention relates to the technical field of high-pressure gas cylinder control, and discloses a high-pressure gas cylinder valve pressure dynamic monitoring and self-adaptive adjusting method. The method comprises the following steps: dividing a high-pressure gas cylinder valve into a plurality of monitoring units, and obtaining pressure, temperature and flow data of each unit to form a comprehensive monitoring data set; establishing a pressure stability evaluation index and an environmental factor influence evaluation index based on the data set, and generating an initial risk value by applying a probability early warning mechanism and an adaptive prediction model; dynamically correcting the initial risk value and outputting a corrected risk value according to spatial relevance or time sequence dependency of adjacent monitoring units; and finally, the corrected risk values are sorted, a priority control instruction list is generated, and a control execution unit is combined to output a self-adaptive adjustment signal so as to adjust the opening degree of the valve. According to the method, comprehensive monitoring and dynamic adjustment of the high-pressure gas cylinder valve are achieved, and the operation stability of the valve is improved.

The invention relates to the technical field of combustion equipment state monitoring, in particular to a combustion state monitoring method and system based on vibration and noise fusion analysis, and the method comprises the steps: collecting a combustion signal in real time through a vibration sensor and a broadband noise sensor, extracting a characteristic frequency band through noise reduction and filtering, and obtaining a combustion state; fPGA hardware-level clock synchronization is adopted to realize time alignment of bimodal signals, a vibration-noise joint feature matrix is constructed and multidimensional coupling is carried out, a combustion state feature vector is generated through a mixed deep learning model, a combustion state is determined based on a bimodal confidence weighting decision, and a linkage control instruction is generated during interruption. The system comprises a signal acquisition synchronization unit, a feature fusion and modeling unit, a state decision and instruction generation unit and a safety linkage execution unit, executes a method and activates a standby safety system, solves the problems of asynchronous bimodal data and insufficient monitoring precision, and improves the combustion state monitoring accuracy and safety response efficiency.

The invention discloses a steam pipeline pressure control method, which comprises the following steps of: acquiring operation information of each key position of a pipe network in real time by constructing a data acquisition system and deploying an execution unit; a multivariable analysis method is utilized, topological structure characteristics of a steam pipeline are combined, a load change model of a steam consumption unit is constructed, the pressure change process caused by operation of different devices is recognized in advance, and a long-term pressure control strategy is made; meanwhile, an accumulative deviation curve is constructed based on a prediction and actual measurement residual evolution sequence, the potential fault risk of the system is identified in an auxiliary manner, and structured alarm output is formed, so that the safety guarantee capability of pipe network operation is enhanced. The steam pipeline pressure control method is suitable for the application scene that the overall steam use requirement is stable, but the local high-frequency disturbance behavior exists, the tiny pressure fluctuation trend can be accurately predicted under the high-disturbance working conditions such as frequent starting and stopping, and it is guaranteed that the steam pressure is stable.

The invention relates to the technical field of AC / DC module control, and discloses an intelligent control method and system for multi-mode switching of an AC / DC module. According to the method, a dynamic topology mapping model is constructed according to an impedance spectrum matrix, and a physical connection relation is converted into a node admittance relation network; generating an energy distribution state sequence based on the power fluctuation vector, and analyzing migration constraint conditions among different operation modes by adopting an association rule mining algorithm; when an operation mode switching instruction is detected, activating a node admittance relation network in the dynamic topology mapping model, and inputting a current energy distribution state sequence to an association rule mining algorithm; according to a real-time matching result of the node admittance relation network and the migration constraint condition, generating a switching control instruction containing an impedance adjustment gradient and a power distribution weight; and the power semiconductor execution unit transmits the switching control instruction to the AC / DC module. According to the method, multi-mode intelligent switching control of the alternating-current and direct-current module can be realized.

The invention relates to the technical field of direct-current power supply fault diagnosis, in particular to a fault self-diagnosis modular direct-current power supply system, which is characterized in that a failure mechanism dynamic modeling unit constructs a multi-stress coupling degradation mapping library, adopts a degradation mechanism decoupling algorithm to separate and superpose failure effects and generates a dynamic degradation model; the early degradation capture unit deploys a high-frequency wide-domain sampling circuit and a noise suppression signal chain, combines time-frequency domain joint feature extraction and separates degradation feature signals from background noise, and the self-adaptive life prediction unit adopts a long short-term memory network and random forest fusion algorithm to dynamically correct parameters and output residual life probability distribution. The intelligent fault judgment unit realizes progressive diagnosis through a hierarchical threshold decision mechanism, and the health management cooperative execution unit triggers a pre-maintenance strategy, executes power flexible derating, redundancy switching or directional fusing isolation, and improves the reliability of the system.

The invention provides an internet of things equipment collaborative task scheduling optimization method and system oriented to edge computing, and relates to the technical field of the internet of things, and the method comprises the steps: collecting a task request from an internet of things terminal, and extracting task features; constructing a dynamic resource state table; generating a candidate node set; selecting a target computing node from the candidate node set, and deploying a task execution unit on the target computing node; generating a resource utilization rate index based on the real-time operation index of the node; and generating a task performance index based on task execution feedback, dynamically adjusting a task scheduling rule, or isolating an abnormal computing node from an abnormal task. Through the task scheduling method and device, the technical problems of low resource utilization rate and high response delay caused by lack of global collaboration of task scheduling in the prior art are solved, and efficient task allocation between edge devices is realized by extracting task features and establishing an optimization model, so that the overall scheduling efficiency and the system performance are improved.

The invention provides an instruction execution method, a processor, electronic equipment and a storage medium, the method is applied to the processor, the processor comprises a first execution unit, a second execution unit and a pipeline register, and a first execution instruction of an atomic instruction group is obtained through the first execution unit; writing a first execution result of the first execution instruction into the pipeline register in response to the first execution instruction; a second execution instruction of the atomic instruction group is obtained through the second execution unit, and the first execution result is read from the pipeline register as the source operand of the second execution instruction in response to the second execution instruction, so that the instruction execution method, the processor, the electronic equipment and the storage medium can reduce the access frequency of the general register and improve the access efficiency of the general register. Processor power consumption and register port conflicts can be reduced.

The invention relates to the technical field of digital twinborn and drainage basin environment monitoring simulation, in particular to a drainage basin environment data simulation deduction system based on a digital twinborn technology, which comprises a digital twinborn management center used for calling multi-source heterogeneous monitoring data of a drainage basin environment to obtain an aligned environment data sequence; the causal coupling analysis unit is used for obtaining a steady-state logic signal or a dynamic decoupling signal; when a dynamic decoupling signal is generated, the topology adaptive reconstruction unit is used for performing structural mutation recognition feedback analysis on a calculation map of the digital twin model to obtain a reconstructed topology map; when the steady-state logic signal is generated, the simulation deduction execution unit is used for performing environment evolution deduction analysis on the aligned environment data sequence based on the current calculation map to obtain a simulation deduction result; according to the method, model calculation divergence or misleading output caused by sensor faults is effectively avoided, and the calculation problem caused by multi-source data heterogeneity is solved.

The invention discloses a source-grid-load-storage integrated coordinated management and control system for an intelligent micro-grid, and relates to the technical field of intelligent micro-grids and comprehensive energy regulation and control. The system comprises the following components: a multi-energy-flow data acquisition unit, a multi-energy-flow coupling modeling and optimizing unit, a multi-energy-flow gradient utilization execution unit, a mode self-adaptive switching unit and a main control unit, according to the invention, through a multi-energy flow coupling modeling and optimization unit, an electric-thermal-gas multi-energy flow coupling model comprising a heat supply network transmission loss calculation sub-model is constructed, a renewable energy consumption constraint sub-model is additionally arranged, and an improved hybrid particle swarm optimization algorithm is adopted to carry out dynamic optimization solution, so that an optimal scheduling strategy is generated; according to the strategy, the consumption rate of renewable energy sources is increased, electricity-heat gradient utilization and efficient configuration are achieved through a heat energy distribution priority regulation and control mechanism and efficiency optimization control of the waste heat recovery module, and dependence of heat loads on electric energy is remarkably reduced.

The invention discloses an end-side semantic navigation method and system based on multi-modal fusion. The method comprises the following steps: a sensing unit obtains real-time environment sensing data and a user original instruction required by navigation; the end side processing unit obtains the dynamic semantic map data set and the real-time pose of the robot, receives the real-time environment sensing data and the user original instruction sent by the sensing unit, and generates a planning path according to the real-time environment sensing data, the user original instruction, the dynamic semantic map data set and the real-time pose of the robot; and the execution unit receives the planned path sent by the end side processing unit, executes the task according to the planned path, and obtains a task execution result. According to the method, diversified navigation instructions can be efficiently, accurately and robustly processed on end-side equipment with limited calculation and storage resources, accurate navigation tasks in a complex and dynamic real environment are completed, and the efficiency, accuracy and task adaptability of end-side navigation are remarkably improved.

The invention relates to the field of intelligent transportation systems, discloses an intelligent highway maintenance system and method, and aims to solve the fundamental defects that in the prior art, the data acquisition dimension is single, the maintenance decision depends on artificial experience, and prospective prediction is lacked. The method comprises the following steps: acquiring multi-modal dynamic sensing data covering a whole road domain; constructing and updating a four-dimensional space-time digital twinborn model in real time; driving the causal graph neural network model to perform structure health state prediction and diagnosis; generating an active maintenance instruction through a multi-objective optimization engine; and the autonomous maintenance execution unit is dispatched to complete unmanned closed-loop operation. According to the method and the system, fundamental transformation of road maintenance from passive response to active prevention, from artificial experience to intelligent decision making and from discrete operation to closed-loop automation is realized, and the maintenance efficiency, the safety and the health level of the whole life cycle of the road are remarkably improved.

The invention relates to the field of automatic control, and discloses an intelligent control dispensing method and system based on automatic optical detection, and the method comprises the steps: obtaining multi-source real-time data of a dispensing production line, carrying out the feature recognition processing through a preset feature extraction model, and obtaining the dynamically updated production state information; then interference filtering processing is carried out to obtain an effective data set, when it is detected that quality fluctuation exceeds a preset range, an alarm is triggered, deviation identification information is generated, self-adaptive parameter optimization processing is carried out in combination with equipment characteristics and historical operation records, and a control instruction is generated; a control instruction is issued to an execution unit through a double-channel transmission mechanism, transmission delay is monitored in real time, and channels are automatically switched; when the output deviation exceeds the tolerance range, a compensation instruction is generated, parameters of the feature extraction model are updated based on the novel quality fluctuation features, and the dispensing control process is iteratively optimized through closed-loop control. According to the method, the dispensing precision and the production efficiency can be remarkably improved.

The invention discloses a three-rotor flow meter device, a control method and a control system, the three-rotor flow meter device comprises a shell, a plurality of rotors and a data acquisition unit, and the data acquisition unit is used for acquiring multi-modal data such as front and back pressure difference, medium temperature, absolute viscosity, vibration spectrum and rotating speed of the rotors in real time. The control system carries out real-time fusion and processing on collected original data through a decision making unit, calculates simulation slip flow based on a digital twinning dynamic compensation model, and corrects pulse equivalent in real time through an execution unit, so that high-precision dynamic compensation of flow output is achieved, and it is ensured that measurement errors are always within a preset range. Meanwhile, the system has an intelligent early warning function, and early fault diagnosis and predictive maintenance can be realized based on multi-parameter threshold comparison. According to the three-rotor flow meter, the measurement precision, stability and reliability of the three-rotor flow meter under the complex working condition are remarkably improved, and the three-rotor flow meter is particularly suitable for flow measurement under the high-viscosity medium condition.

The invention discloses a stainless steel thin-wall pipe bending forming production line control method and system. The method comprises the steps that state feature vectors are generated through data collection; establishing a hybrid network model, and optimizing hyper-parameters of the hybrid network model by using an OOA eagle optimization algorithm to obtain an improved hybrid network model; inputting the state feature vector into an improved hybrid network model, and outputting predicted values of the springback value, the wrinkling probability and the ovality which are about to occur in the current bending section; inputting the predicted value into a multi-objective optimization algorithm, and performing back calculation in real time to obtain an optimal compensation parameter set by taking minimization of springback, wrinkling risk and ovality as optimization objectives; and the compensation parameter set is transmitted to a physical execution unit for production line control including bending control, core rod control, clamping control and feeding control. And the first-pass yield of bending forming and the production control efficiency are improved.

The invention provides an intelligent control method and system for discrete event simulation of an aluminum template production line, and relates to the technical field of data processing.The method comprises the steps that a material transfer path instruction and a cutting process key point coordinate sequence are analyzed from a dynamic anti-interference scheduling strategy, the key point coordinate sequence is input into a curve driving algorithm, and a curve driving algorithm is obtained; a parameterized continuous cutting path curve is generated according to the aluminum template contour precision requirement and the cutting head dynamics constraint; the method comprises the following steps: performing equal-bow-height error discretization processing on a continuous cutting path curve to generate a discrete pose point set with a timestamp, superposing AGV dynamic constraints to generate a speed look-ahead control instruction set, splitting the instruction set into independent control units according to process sections, packaging the independent control units into a time trigger message through an industrial bus protocol, and sending the time trigger message to a server; and the simulation clock synchronization signal is injected into a physical execution unit. According to the invention, intelligent and accurate control of the aluminum template production process is realized, and the production efficiency is effectively improved.

The invention relates to the technical field of geological information, and discloses a precise positioning method and system based on geological mineral product three-dimensional modeling, and the method comprises the steps: deploying a laser ranging array, a micro-seismic array and a fiber grating sensing array, and synchronously collecting multi-source deformation and stress data; after space-time alignment and filtering, an improved ICP algorithm is adopted to reconstruct a millimeter-level precision three-dimensional point cloud model; a safety coefficient cloud picture is calculated by combining a geomechanical constitutive equation, and a risk area is automatically delineated according to a three-level threshold value; and closed-loop control instructions such as voice alarm, thermodynamic diagram pushing, grouting coordinates and avoiding paths are generated. The system is composed of a sensing deployment unit, a data preprocessing unit, a point cloud reconstruction unit, a mechanical analysis unit, a risk delineation unit and an instruction distribution and execution unit. Millimeter-level positioning, sub-hour-level early warning and full-automatic intervention of goaf deformation are achieved, the early warning accuracy rate exceeds 95%, the emergency response is shortened to the minute level, and mine safety and mining efficiency are improved.

The invention relates to the technical field of feed efficiency optimization, in particular to a feed efficiency optimization system based on a growth model, and the system comprises a data collection unit which is used for collecting and associating individual identities, individual weight data, time-phased feed intake data, environment data and environment forecast data; the model calibration unit is used for calibrating individual energy conversion efficiency parameters, generating an environment dynamic correction factor by using the environment data and generating a group classification label based on historical individual performance data; and the decision and execution unit operates a double-cycle cooperation mechanism based on pre-judgment and execution. By dynamically calibrating the individual energy conversion efficiency and combining with the environment forecast, real-time calculation and differentiated feeding regulation and control of the individual energy demand can be realized.