867 results about "Adaptive switching" patented technology

The invention discloses an Internet of Things data transmission optimization system based on intelligent routing, and relates to the technical field of data transmission optimization. The heterogeneous network access module supports adaptive switching of multiple protocols such as 5G, NB-IoT and LoRa, and monitors link quality; intelligent routing decision selects an optimal path by means of deep reinforcement learning, and end-to-end delay is reduced; data distribution scheduling split data streams are transmitted in a multi-path parallel mode, and forward error correction is combined to guarantee reliability. The network state prediction uses an LSTM to predict bandwidth, and a route is adjusted in advance; the secure transmission integrates iris authentication and dynamic encryption. According to the invention, efficient, reliable and secure transmission is cooperatively realized, energy collection and dormancy strategies are combined, the equipment endurance is greatly prolonged, the network energy consumption is reduced, and the method is suitable for industrial control, telemedicine and other scenes with high requirements on real-time performance and reliability.

The invention discloses a method for realizing data communication by a multi-band adaptive antenna based on 5G communication. The method comprises the steps of dynamic sensing and multi-mode signal fusion, multi-band intelligent analysis, hybrid beam forming and dynamic reconstruction, cross-layer parameter joint tuning, real-time monitoring and self-learning compensation, multi-band adaptive switching and fault diagnosis and error calculation. The problems that in traditional 5G communication, the antenna frequency band is fixed, the beam adjusting capacity is limited, the anti-interference performance is poor, and the communication performance is unstable in a complex environment are solved. According to the method, the channel state is accurately evaluated and predicted through fusion of the extended Kalman filtering algorithm and the convolutional neural network, and frequency band resources are intelligently allocated and optimized through the deep Q network DQN and the non-orthogonal multiple access NOMA principle, so that omnibearing optimization and management of the antenna are realized, the communication efficiency and stability are improved, and the method is suitable for large-scale popularization and application. And the efficient and stable operation of the communication system in various complex environments is ensured.

The invention relates to the technical field of data center heat dissipation, and particularly provides a single-phase and two-phase immersion liquid cooling method and system based on AI intelligent decision, and the method comprises the steps: injecting coupled data into a dynamic feature extraction engine, and outputting a thermodynamic state evolution tensor which comprises the characteristics of a temperature change rate, a load-heat flux density coupling coefficient and the like; the thermodynamic state evolution tensor is input into the deep neural network model, the temperature and pressure matched with the current thermodynamic state evolution tensor are calculated, and a closed-loop control instruction set capable of being executed by equipment is generated; a closed-loop control instruction set is injected into an execution mechanism set, execution mechanisms execute power reconstruction and flow channel switching according to instructions, gaseous fluorinated liquid is liquefied and flows back through an efficient condenser in a two-phase mode, and heat dissipation mode self-adaptive switching and heat cycle reconstruction are achieved. The system comprises a server, an AI algorithm controller, a cooling liquid storage device, a condenser, a circulating pump, an electric valve, a pressure release valve and a temperature sensor. The heat dissipation efficiency and the system reliability are remarkably improved.

The invention provides a low-power-consumption communication method and system based on a 5GRedCap portable WiFi network, and relates to the technical field of low-power-consumption communication, and the method comprises the steps: carrying out the adaptive control of a DRX period predicted according to WiFi access flow, setting the wake-up control of a RedCap communication terminal module, setting the adaptive switching of a bandwidth part, and setting the switching operation of a cellular communication system. By introducing a DRX cycle adaptive control mechanism based on WiFi access flow prediction and an intelligent wake-up strategy of uplink and downlink linkage into a 5G RedCap communication terminal module, the DRX sleep and wake-up cycle can be dynamically adjusted according to the actual service load state, the power consumption during the idle period of the terminal is effectively reduced, the energy waste caused by frequent wake-up is avoided, and the user experience is improved. And precise energy-saving management of the honeycomb side is realized.

The invention discloses an unmanned aerial vehicle cluster cooperative control system and method, and relates to the technical field of unmanned aerial vehicle cluster control, and the system comprises a multi-source sensing unit, an integrated laser radar, an RGB-D camera and a millimeter wave radar array, and is used for generating a centimeter-level precision three-dimensional map and tracking a dynamic obstacle in real time; the collaborative decision-making unit comprises an artificial intelligence prediction module which is used for predicting a collision point based on the movement state of the obstacle; the safety space construction module is used for generating a dynamic safety sphere according to the formation envelope size and the obstacle volume; the artificial intelligence correction module is used for constructing a safety section and generating an optimization path; and the dynamic communication unit is used for carrying out cluster data interaction by adopting hybrid networking of 5G and LoRa adaptive switching. The unmanned aerial vehicle cluster can be used as a whole to avoid obstacles, the original forms of the unmanned aerial vehicles are synchronously kept, and it is guaranteed that multiple unmanned aerial vehicles in the cluster do not collide.

The invention relates to the technical field of audio pushing optimization, and discloses an audio stream pushing crash prevention method and system, a storage medium and equipment, and the method comprises the steps: dynamically generating a format perception pre-detection thread based on a current audio stream format, and adjusting the priority and sampling frequency of the thread according to the audio bit depth and the sampling rate; multi-level intelligent buffer area management with sound quality priority is implemented, and dynamic adjustment of the size of a buffer area and adaptive switching of the buffer area are realized according to different audio formats and input source types; establishing a cross-brand equipment coordination framework, and setting a watchdog timer with preferential tone quality to perform timed monitoring so as to process response timeout; triggering an elegant degradation strategy of sound quality priority according to the resource availability score, and reducing the audio processing quality step by step to maintain the playing continuity; the key characteristics of each transmission protocol are identified and utilized, the transmission protocols are adaptively switched according to the network condition and the processing capability of brand equipment, and real high-quality and zero-interruption audio experience is brought to users.

The invention discloses a self-adaptive focal length control method and system of a laser cutting machine, particularly relates to the technical field of laser processing, and is used for solving the problem of insufficient laser focus control precision caused by separation of a detection point and a processing point and geometric mutation of a curved surface in the prior art. Instantaneous distance information is continuously obtained in the moving process of a cutting head, the curvature change characteristics of the surface of a workpiece are calculated according to the instantaneous distance information, inertial load and dynamic load risks caused by geometrical morphology sudden change are comprehensively evaluated, and a path risk section and a non-path risk section are intelligently recognized. A conservative prediction mode and a conventional prediction mode are adaptively switched according to different risk sections, the normal vector of the surface of a workpiece at a laser machining point is accurately predicted, then the posture adjustment amount of the cutting head is calculated according to the normal vector, and finally real-time and smooth posture adjustment of the cutting head in the movement process is achieved through multi-axis synchronous interpolation control. And the laser beam is always vertical to the surface of the workpiece.

The invention provides a water resource emergency-normal management mode adaptive switching method based on machine learning, and the method comprises the following steps: obtaining original monitoring data, carrying out the preprocessing, and constructing spatial-temporal features; performing scene division and dynamic threshold learning based on the feature matrix; an improved LSTM network model is adopted to predict a system state and optimize a switching time sequence; and generating a decision scheme based on multi-objective optimization and performing risk assessment. According to the method, intelligent switching of the management modes is realized through a multi-source data fusion and deep learning method, the scientificity and reliability of switching decisions are improved, and the practical value is relatively high.

The invention discloses a network-following flexible switching prediction control method and system for a grid-connected converter, and the method comprises the steps: calculating a short-circuit ratio according to obtained power grid impedance data, and if the short-circuit ratio is smaller than a switching threshold value, indicating that the power grid strength is reduced, and otherwise, indicating that the power grid strength is not reduced. At the moment, a prediction control reference current in the switching process is calculated through an adaptive switching function in a first switching function form and a current reference value in a grid following mode and a grid construction mode, and the grid-connected converter is switched from the grid following mode to the grid construction mode; and otherwise, indicating that the power grid intensity is increased, calculating a prediction control reference current in a switching process through an adaptive switching function in a second switching function form and a current reference value in a grid following mode and a grid construction mode, and switching the grid-connected converter from the grid construction mode to the grid following mode. Therefore, according to the adaptive switching function-based network-following flexible switching prediction control method, rapid and flexible switching of the grid-connected converter module is ensured, and the stability and the grid-connected output efficiency of the converter in the new energy grid-connected power generation system can be effectively improved.

The invention discloses a self-adaptive switching system and method for power supply and distribution, and the system comprises a data collection module, an analysis module, a modeling module, a decision module, an execution module and an early warning module, and the data collection module generates a real-time monitoring signal; the analysis module performs dynamic weighted evaluation according to the real-time monitoring signal and generates an evaluation signal; the modeling module constructs a virtual model of the power supply system and generates an analog signal; the decision signal generates a control signal containing a path selection instruction; the execution module controls the semiconductor device to switch the physical connection state of the power supply according to the control signal and generates a switch feedback signal; and the early warning module forms an equipment health assessment result according to the switch feedback signal and an abnormal arc signal when the execution module switches the connection state and transmits the result to the decision module. The self-adaptive switching system for power supply and distribution can solve the problem that a traditional power supply and distribution switching system depends on a single threshold to judge the state of a power supply and cannot comprehensively evaluate the quality of electric energy.

The invention discloses a shield tunneling intelligent control method and system based on a ground-tunnel-machine-information-human adaptation level, and relates to the crossing field of underground engineering intelligent construction and information technology. Setting a first-level index and a second-level index; dividing the shield construction state into three levels of intelligent adaptation levels by combining the total score of the construction state with a dynamic threshold value; setting four targets of attitude intelligent control, settlement intelligent control, tunneling efficiency optimization and construction abnormity diagnosis; a machine learning prediction model is constructed, and the construction state parameters corresponding to the targets are predicted in real time; according to the shield intelligent adaptation level, the man-machine cooperation modes are divided into three classes of A level, B level and C level; and comparing actual data with a prediction result of the machine learning model, and dynamically correcting the control target parameters. Through real-time sensing of geological, mechanical and environmental multi-dimensional construction states and in combination with a dynamic weight distribution strategy, precise evaluation of the shield construction state is achieved, and meanwhile self-adaptive switching of the man-machine control right is achieved.

The invention discloses a multi-energy access electric power spot market risk prevention and control method and system, and relates to the technical field of risk prevention and control, and the method comprises the following steps: obtaining multi-dimensional data of a to-be-detected region, obtaining the risk conduction intensity between nodes based on a power grid topological structure, and constructing a dynamic risk topological network model; according to the risk topology network model, node operation characteristics are extracted, evaluation data of the classification model are synchronously obtained, model switching regulation and control are carried out according to the evaluation data, and the model switching regulation and control further comprises the step of outputting a risk overflow probability; through the method based on the dynamic risk topology network and multi-model adaptive switching, the problems of inaccurate risk prediction and untimely response in multi-energy access to the electric power spot market are solved.

The invention relates to the technical field of adaptive routing optimization, and particularly discloses a topology self-recognition and routing optimization method and system for a concentrator and a collector, and the method comprises the steps: carrying out the topology modeling of a multi-layer heterogeneous network; executing high-speed link state monitoring based on the initial topological graph; global loop detection and link optimization are completed based on the link state matrix; a weighted robust multi-dimensional path selection method is adopted to configure a main path and a standby path for each node; and monitoring the path state in real time and dynamically switching. In the prior art, a static route, a single protocol path or a fixed main and standby path is mainly adopted, and especially in a cross-protocol, cross-level and dynamic multi-node environment in a smart city, rapid path switching and high-reliability route redundancy guarantee cannot be realized. Due to the fact that the multi-dimensional path selection method and the cross-level and cross-protocol self-adaptive switching strategy are adopted, the problems of routing interruption and performance reduction are avoided, and the reliability of the whole network is improved.

The invention discloses a soil environment multi-parameter monitoring system and method based on an intelligent sensor, and aims to solve the problems of single sensing dimension, shallow data analysis and lack of automatic closed loop in the prior art. The system comprises an intelligent sensor node, a data processing center and a user terminal. The nodes can synchronously acquire multi-parameter data, perform local preprocessing and adaptively switch communication protocols. And the data processing center adopts a space-time fusion analysis model to carry out deep fusion and prediction on the uploaded data. And when the user terminal receives the prediction result and meets a preset condition, the user terminal sends a control instruction to the node, and after the node receives the instruction, the external agricultural equipment is linked to execute operation. According to the invention, by constructing a collaborative architecture of intelligent perception, cloud prediction, man-machine collaboration and closed-loop control, comprehensive, accurate and automatic management of the soil environment is realized, and the resource utilization efficiency is significantly improved.

The invention relates to the technical field of micro-grid control, and discloses a micro-grid adaptive switching control method and system based on multi-source information fusion, and the method comprises the steps: obtaining the internal operation parameters of a micro-grid, and the state parameters of an external large power grid connected with the micro-grid; the internal operation parameters and the external large power grid state parameters are fused, and a real-time comprehensive operation state model of the micro-grid is constructed; based on the real-time comprehensive operation state model, adaptively determining a target operation mode of the micro-grid; and according to the target operation mode, generating and executing a smooth switching control instruction of the micro-grid. According to the method, the multi-source operation information of the large power grid inside and outside the microgrid is fused, the comprehensive operation state model is constructed in real time, the target operation mode is adaptively selected, and switching control is smoothly executed, so that efficient and stable switching of the microgrid between multiple modes such as grid connection and island can be realized, and the power supply reliability, the system stability and the electric energy quality are improved.

The invention relates to the technical field of pipeline construction, and discloses a water supply pipeline installation auxiliary method and system based on multi-source positioning and digital twinning, and the method comprises the steps: constructing a digital twinning model based on a three-dimensional point cloud modeling technology, and integrating the geographic information of a construction area, high-voltage line corridor parameters and pipeline installation path planning data; a multi-source positioning sensor group is arranged on hoisting equipment and a pipeline, and spatial position, attitude and environmental electric field data are collected in real time and transmitted to a data fusion processing module; multi-source data are fused through a Kalman filtering algorithm, and high-precision positioning is realized in combination with a digital twinborn model; dynamically adjusting a positioning mode based on an adaptive switching mechanism; construction parameters are generated in real time, dynamic monitoring and early warning are conducted, and when a safety threshold value is exceeded, alarm is triggered and operation is suspended. The system corresponds to the method. By the adoption of the method, the positioning precision and environmental adaptability of water supply pipeline installation are improved, the construction safety risk of complex scenes is reduced, and the construction efficiency is improved.

The invention relates to the technical field of power supplies, and discloses a high-precision power supply control method based on a digital signal processor, which comprises the following steps of: acquiring data such as voltage, current, temperature, load parameters and the like of a power supply at a high frequency of kHz-level sampling frequency by using a high-precision sensor, transmitting the data to a DSP (Digital Signal Processor), and removing noise interference by applying wavelet transform. Sample quality is improved through high-frequency data acquisition and wavelet denoising, nonlinear and time-varying characteristics of a power supply system are identified by using a lightweight CNN and an LSTM, a reinforcement learning dynamic optimization control strategy is combined, self-adaptive switching between traditional control and an intelligent algorithm is realized through working condition identification, and the power supply system control method based on the LSTM is realized. The problems that noise interference influences data quality, system characteristic recognition is inaccurate, complex working condition control strategy optimization is insufficient, and output precision is unstable due to the fact that a control mode cannot be switched in a self-adaptive mode in an existing power supply can be effectively solved, and control precision, efficiency and device loss can be considered. And the stable operation requirement of the high-precision power supply under multiple working conditions is met.

The invention relates to the technical field of vehicle-mounted communication, and provides a 5G network optimization method and system based on an intelligent dynamic multi-band adaptive switching system, and the method comprises the steps: obtaining network signal quality data, environment perception data and a communication task demand set; dynamic obstacle features are extracted based on environmental perception data, and signal occlusion prediction is output through a double-branch mixed deep learning model; screening qualified candidate links to construct a candidate set, generating a dynamic weight by using a deep Q network, calculating a comprehensive score of the candidate links, determining a main communication link under a hard constraint condition, and generating a communication strategy instruction; and according to the instruction type of the communication strategy instruction, performing dynamic fragmentation and multi-path distribution on the data stream, performing verification, recombination and intelligent delivery by a receiving end, and collecting a feedback data packet for network closed-loop feedback. According to the method, multi-mode sensing and signal shielding prediction are fused, and a self-adaptive switching and cooperative transmission mechanism of multi-band communication is constructed.

The invention relates to the technical field of ship fuel supply system control, and discloses a double-lug C-type LNG fuel cabin and methanol fuel self-adaptive switching control system which comprises a fuel state sensing module, a switching logic decision module, an execution mechanism control module, a safety redundancy guarantee module and a state feedback recording module. The fuel state sensing module is used for collecting and normalizing fuel parameters and mechanism operation state data; the switching logic decision module constructs a model to simulate switching dynamic characteristics and mechanism response characteristics, and performs parameter adaptation; the execution mechanism control module constructs an algorithm to calculate a switching pressure threshold value, a flow matching value and a valve group action time sequence; the safety redundancy guarantee module evaluates the switching safety; and the state feedback recording module performs secondary evaluation in combination with the environment working condition when the risk exists. According to the system, self-adaptive switching of LNG and methanol fuel is achieved, and switching accuracy, system stability and safety are improved.

The invention particularly relates to an emergency broadcast intelligent early warning system applied to disaster prevention and reduction, and relates to the technical field of communication. The redundant communication and self-adaptive switching module is configured to establish a multi-mode communication network, monitor link states in real time and realize intelligent switching and load balancing of communication links based on a dynamic algorithm; an intelligent decision-making and precise broadcasting module; and the emergency broadcast terminal is connected with the offline execution module. According to the invention, a multi-mode communication architecture is established, each link monitors parameters such as signal strength, delay, packet loss rate and the like in real time, and intelligent switching and load balancing are carried out through a dynamic routing algorithm; and meanwhile, on the basis of memory thermal characteristic analysis, link faults are pre-judged in advance, switching is triggered, the success rate of early warning information transmission under extreme disasters is ensured, and the problem of transmission interruption of traditional single link communication under the scenes of power failure, base station damage and the like is solved.

The invention relates to the technical field of charging management, in particular to a front-and-back stage cooperative control method and system for a direct-current fast charging module. Monitoring working parameters and environmental parameters of the front-stage converter and the rear-stage converter in real time, and constructing a multi-dimensional feature matrix in combination with a pre-acquired historical charging mode and user behavior data; performing multi-scale prediction on the charging energy demand by adopting the trained time sequence prediction model, and generating an energy demand curve comprising a short-term prediction window and a long-term prediction window; and dynamically adjusting modulation parameters of the front-stage converter according to the energy demand curve of the short-term prediction window, and adaptively switching a hybrid modulation mode of the rear-stage converter based on the energy demand curve of the long-term prediction window. According to the invention, working parameters and environmental parameters of the front-stage converter and the rear-stage converter can be monitored in real time, and comprehensive analysis is carried out in combination with a historical charging mode and user behavior data, so that more accurate and efficient charging control is realized.

The invention relates to the technical field of equipment control, in particular to a self-adaptive switching method and system of a current transformer. The method comprises the following steps: collecting primary side parameters and secondary side parameters of a current transformer to obtain primary side collection parameters of the current transformer and secondary side collection parameters of the current transformer; performing current transformer state segmentation initialization according to the current transformer primary side acquisition parameters and the current transformer secondary side acquisition parameters so as to generate a current transformer operation state classification result; switching result judgment is conducted on the current transformers according to the current transformer operation state classification result, and when the current transformer operation state classification result is a current transformer critical state section, parallel buffering is conducted on the current transformers, and critical current transformer switching data are generated. According to the invention, through accurate state classification, a dynamic switching mechanism, time sequence optimization and visual display, the switching sensitivity and stability of the current transformer are improved.

The invention discloses an industrial channel test method and system based on a serial port communication protocol, relates to the field of industrial control, and aims to solve the problems that an existing channel test system is single in connection mode, complex in deployment and low in efficiency due to dependence on manpower. According to the invention, the deployment process is simplified by supporting dual-protocol adaptive switching between an RTU mode (direct connection through a 485-to-USB converter) and a TCP / IP mode (wired / wireless network access); driving test logic by using a spreadsheet, pre-defining a Modbus address, a measuring range and a precision threshold value, automatically generating an instruction and acquiring data; and the integrated voice broadcast module feeds back a test result in real time, and prompts an abnormal channel and a processing suggestion when the precision exceeds the limit, so that single-person closed-loop operation is realized. The test result is automatically recorded to the spreadsheet, a structured report is generated, historical tracing and cross-platform data integration are supported, the test efficiency and reliability are remarkably improved, and the method is suitable for high-frequency channel verification of the industrial control system.

The embodiment of the invention provides a communication mode self-adaptive switching method, device and equipment based on an SDN (Software Defined Network), which are applied to the technical field of communication, and the method comprises the following steps: receiving a real-time monitoring result reported by a channel state monitoring unit, analyzing the monitoring result, and obtaining a current communication demand and channel state information; according to the current communication requirement and the channel state information, whether communication mode switching or transmission resource redistribution needs to be carried out is judged; if communication mode switching or transmission resource redistribution needs to be carried out, a corresponding instruction is generated and sent to the programmable network equipment; therefore, after the programmable network device receives the instruction, corresponding operation is executed, and communication mode switching or transmission resource redistribution is completed. In this way, the limitation of a single communication mode and fixed transmission resource allocation can be effectively overcome, and a more reliable, efficient and flexible communication solution is provided for the modern communication field.

The invention discloses a heaven and earth integrated multi-domain fused automobile high-level automatic driving system, which realizes more comprehensive and non-blind area sensing and positioning of an automatic driving automobile through multi-domain information fusion of a satellite platform, a cloud platform, an automobile end platform and the like, and promotes comprehensive upgrading of comprehensive efficiency of an intelligent traffic system. Based on cloud error broadcasting and a high and low orbit satellite fusion enhancement technology, the problem of automobile communication loss caused by the fact that single ground communication is limited by base station coverage is solved; based on a cloud reinforcement learning architecture, the communication network stability of the space-ground integrated system is improved through adaptive switching of a communication network; a road semantic map and a decision control experience pool are established based on vehicle high-precision trajectory data collected by a platform, and the limitation of the perception ability of a bicycle is made up through cloud federal learning scene experience sharing; the vehicle end dynamically updates a cloud road semantic map through local map matching; and information support is provided for realizing a chassis multi-domain fusion technology based on high-precision positioning.

The invention relates to the technical field of digital communication, in particular to an intelligent electric meter dual-mode communication self-adaptive switching method, and provides the following scheme that operation data are divided into a plurality of data units at a sending end, priority evaluation is carried out, and the time delay, the packet loss, the energy consumption and the bandwidth state of two links are combined, so that the intelligent electric meter dual-mode communication self-adaptive switching method is realized. Positioning clearing cut points in the priority sequence so as to realize differentiated distribution of data between a high-speed link and a low-speed link; when the data is distributed to a low-speed link, sparse coding is carried out by adopting a preset dictionary, and differential updating is triggered when an error exceeds a limit, so that decoding consistency is ensured; a receiving end realizes unified analysis of the original message and the sparse message through cross-link association, dictionary updating and residual synthesis; according to the invention, the timeliness of the key message can be guaranteed, the communication energy consumption and cost can be reduced, and the cooperation efficiency of the dual-mode link can be improved.

The invention relates to the technical field of network switching, in particular to a heterogeneous network dual-mode self-adaptive switching method and system. According to the method, the multi-dimensional environment sensing data and the initial signal intensity are acquired, the signal intensity is calibrated in a dynamic environment, the dynamic response of the network state to real-time change is realized, and the connection state characteristic and the side network consumption index of the dual-mode network are acquired by combining the calibrated signal intensity. The switching demand can be evaluated on the basis of the data, the switching demand can be judged more accurately through the multi-dimensional evaluation mechanism, the limitation that a traditional method only depends on signal strength is avoided, and the switching demand can be evaluated by generating an optimized directed time sequence graph and a network performance data set. According to the technical scheme, the equipment can make a scientific and reasonable switching decision according to the real-time network state snapshot data and the preset optimization time sequence graph, and the connection requirements of the equipment can be dynamically adapted under the variable network conditions, so that the optimal network experience is kept, and the network connection quality is improved.

The invention relates to the technical field of integrated circuit design and artificial intelligence hardware acceleration, and particularly discloses a quantization precision adaptive switching hardware acceleration architecture. Hardware is divided into a static area and a dynamic area, wherein the static area comprises a pre-processing module, a post-processing module, a control module and a reconfigurable logic loading control block which run all the time; the dynamic region bears a quantization and inverse quantization module composed of a plurality of reconfigurable logic units. The method is characterized in that a model precision instruction is analyzed through a control module, and a reconfigurable logic file with corresponding precision is dynamically loaded to a dynamic area from external starting equipment by a loading control block according to an internal address mapping table, so that seamless switching of different quantization precision is realized on the hardware level, and the whole chip does not need to be burnt again. According to the design, through a modularized and reconfigurable hardware structure, the limitation of fixed-precision hardware in coping with diversified AI models is effectively solved, and the deployment flexibility and energy efficiency are remarkably improved.

The invention discloses a casting production line energy consumption optimization control method, which comprises the following steps of: deploying intelligent sensing nodes in each process unit, acquiring and marking various process data in real time, and establishing a multi-target feature modeling and optimization framework based on layered multi-agent by adopting data preprocessing means such as time sequence synchronization, exception elimination and normalization; balanced optimization of key indexes such as energy consumption, productivity, quality and safety is achieved, the system has the capabilities of parameter self-adaptive switching, real-time response, rolling optimization and continuous self-lifting, and the operation efficiency of a production line, energy consumption control and robustness under complex working conditions are effectively improved.

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.