1533 results about "Dynamic energy" patented technology

The invention discloses an energy consumption model intelligent construction system and method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the system comprises an Internet of Things multi-source data collection module, a data cleaning and space-time calibration module, a multi-source data semantic fusion module, a dynamic energy consumption relation graph construction module, an intelligent decision engine module and an edge-cloud collaborative deployment module. According to the method, multi-source data are fused through a Transform multi-head self-attention mechanism, a dynamic energy consumption relation graph is constructed by using a graph neural network, and dynamic modeling and intelligent regulation and control of energy consumption are realized in combination with an edge-cloud hierarchical decision architecture; the method comprises the steps of data acquisition and standardization, cleaning calibration, semantic fusion, graph modeling, hierarchical decision making and collaborative execution. According to the method, the problems of insufficient data integration and model staticization of a traditional system are solved, the accuracy, real-time performance and global optimization capability of energy consumption management are improved, the method is suitable for scenes such as intelligent buildings, the energy efficiency is remarkably improved, and the data security is guaranteed.

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention discloses an exoskeleton motion signal peak valley intelligent detection method and system, and relates to the technical field of computer assistance. The method is used for solving the problems of control delay and high misjudgment rate caused by large motion signal noise interference and inaccurate processing in an exoskeleton system. The method comprises the steps of firstly, suppressing motion artifact noise and generating a high-signal-to-noise-ratio preprocessing signal through multi-modal signal collaborative noise reduction and dynamic energy entropy segmentation, secondly, constructing a parallel convolution attention network to extract multi-modal features, and extracting peak and valley candidate points in combination with dynamic weight fusion and multi-scale differential detection; a peak valley point set is optimized based on a variable structure density sensing clustering algorithm and density gradient analysis, artifact interference is eliminated, finally, a multi-rule confidence model is constructed by fusing time sequence prediction of a bidirectional gating circulation unit and biomechanical correlation, and a threshold value is dynamically adjusted to trigger an exoskeleton joint assistance instruction. Closed-loop processing from signal acquisition to real-time control is realized, and peak valley detection precision and response speed are remarkably improved.

The invention discloses a method and a system for monitoring energy conversion carbon measurement based on dynamic carbon monitoring. The method comprises the following steps: acquiring energy flow data, carbon emission parameters, equipment working conditions and environment data in real time; constructing a carbon emission correction model containing a dynamic energy conversion coefficient and real-time fuel components; adopting an efficiency compensation function and a working condition self-adaptive correction function related to an equipment load rate; and visually displaying the real-time numerical value of the carbon emission and triggering the early warning of the carbon emission based on the model output. The system comprises a multi-source data real-time acquisition module, a dynamic carbon measurement model and a real-time monitoring and early warning platform, the method has the advantages of accurate decision support, environmental supervision enhancement and adaptation to energy transformation.

The invention relates to the technical field of industrial equipment temperature control, and discloses a temperature control system, which comprises a multi-source state sensing module for acquiring a power load, temperature, humidity and feedback temperature; the feed-forward prediction and analysis module is used for generating a feed-forward signal based on the load and the temperature; the closed-loop feedback and safety guarantee module is used for calculating a feedback signal according to the temperature deviation; the cooperative control decision module combines the feed-forward signal and the feedback signal to generate a fan instruction, and generates a baffle instruction for balancing heat dissipation and regeneration according to the feed-forward signal and humidity; the self-adaptive heat dissipation execution module is used for executing a fan instruction to generate cooling airflow; and the dynamic energy regeneration module executes a baffle instruction to distribute the waste gas flow direction. According to the method, the power load is introduced to serve as a prospective index, correction is conducted in combination with the environment temperature, a predictive heat dissipation control model is constructed, the system can start heat dissipation adjustment before a large amount of heat is generated, and therefore smooth and stable control over the equipment temperature is achieved.

The invention discloses a water-cooling energy consumption adaptive regulation and control method suitable for a data center, and the method comprises the following steps: obtaining a multi-dimensional operation parameter set of the data center in real time, and generating a real-time thermal load density distribution diagram reflecting the heat distribution condition in a machine room; calculating the overall thermal deviation degree and the local hot spot intensity, and solving through a dynamic energy efficiency optimization function to obtain a target system supply and return water temperature difference and a target cooling liquid flow; inputting into a hydraulic and thermodynamic simulation model, and generating an optimal multi-device coordinated regulation instruction set; and executing the multi-device coordinated regulation instruction set, and then performing parameter calibration on the hydraulic and thermodynamic simulation model. The method has the following advantages and effects that the thermal environment of the machine room can be sensed in real time, the energy consumption and heat dissipation requirements can be intelligently balanced, and multiple devices in the cooling system can be cooperatively regulated and controlled, so that the overall energy consumption of the cooling system is optimized while the local overheating risk of the machine room is thoroughly eliminated.

The invention relates to the technical field of medical equipment control, in particular to a control system and a control method of cornea crosslinking equipment. The control system comprises a multi-modal data fusion module which is configured to integrate a preoperative corneal topographic map curvature matrix, OCT layering thickness data and biomechanical parameters to generate a four-channel fusion tensor; the virtual cornea modeling module is configured to input a conditional generative adversarial network to generate a virtual cornea model under the condition of the four-channel fusion tensor; the dynamic energy planning module is configured to generate a thermodynamic diagram of energy distribution based on mechanical response prediction of the virtual cornea model; and an illumination control module configured to perform patterned illumination based on the thermodynamic diagram. According to the method, the virtual cornea model is constructed, the energy distribution thermodynamic diagram is generated, accurate patterning reference can be provided for irradiation treatment, and a better treatment effect is achieved.

The invention provides a heat storage heat pump system control method based on a physical information neural network, and belongs to the technical field of heat storage pump system intelligent control. Aiming at the problems that in the prior art, an algorithm is difficult to adapt to dynamic energy consumption requirements, engineering application of a model is difficult due to building space heterogeneity, high-order RC model prediction credibility is weak, engineering feasibility is poor and the like, a solution combining a physical information sequence to sequence neural network technology and a finite-state machine control strategy is provided. On the model level, a 2R2C resistance-capacitance RC model of building temperature change is established, and then a PI-Seq2seq prediction model is proposed based on the physical model. On the control flow optimization level, on the basis of an industrial and commercial time-of-use electricity price policy, an FSM control model is designed, a system state set is defined, parameters and a transfer function are input, and a control rule is constructed in combination with the working period of a building heat pump and the characteristics of a heat storage tank. And finally, energy consumption cost optimization and indoor temperature stabilization under the peak-valley electricity price are realized.

The invention relates to the field of energy management, and discloses a distributed energy intelligent configuration method and device based on Internet of Things big data, and the method comprises the steps: collecting the real-time energy flow and connection state of each node, integrating the real-time energy flow and connection state into initial node embedding, constructing a graph model, calculating the mutual influence weight of the nodes, and adjusting the interaction frequency; based on the mutual influence weight, the interaction frequency and the graph model, performing feature extraction on each node state, generating a node state feature vector, identifying a potential fault starting point exceeding an abnormal fluctuation threshold, predicting a propagation trend, and generating a risk path and a priority; extracting a hub based on a risk path and a priority, estimating an influence range and simulating diffusion, calculating system stability, if the system stability is lower than a threshold value, generating a resource optimization scheme, fusing position marks to generate an adjustment verification vector, updating an abnormal fluctuation threshold value, and reevaluating a potential fault starting point to obtain an adjusted propagation path; according to the invention, accuracy and robustness of dynamic energy management can be realized.

The invention belongs to the technical field of intelligent energy control, and particularly relates to an energy equipment control method based on regional people number distribution, which comprises the following steps: acquiring real-time people number distribution data, environmental parameter data, historical energy consumption data and historical people flow characteristic data, and predicting regional people number distribution data in different time periods; based on historical energy consumption data, historical people flow characteristic data, real-time people number distribution data and environmental parameter data, a dynamic energy demand model is constructed, regional people number distribution data in different time periods are combined, a people number density thermodynamic diagram is generated, regional energy equipment load change is predicted, and a regional energy demand model is established according to the load change and the people number density thermodynamic diagram. A differential control strategy is generated, and operation parameters of the energy equipment are dynamically adjusted; and regulating and controlling the energy equipment according to the differentiated control strategy, synchronously collecting regulated and controlled regional environment feedback data and equipment energy consumption data, and generating an energy efficiency evaluation report. Therefore, the problems of high response delay, limited parameter matching precision and the like in the prior art are solved.

The invention discloses a building heating, ventilating and air conditioning control method with dynamic energy efficiency optimization, and belongs to the technical field of heating, ventilating and air conditioning control. The method comprises the following steps: step 1, acquiring temperature, humidity, carbon dioxide concentration and personnel distribution data in real time, and performing preliminary data processing through an edge computing node; step 2, realizing accurate prediction of future load change of the building; step 3, generating an optimal control strategy by using a reinforcement learning algorithm, and realizing dynamic optimization control of equipment operation; 4, the compliance of equipment operation, network communication and control instructions is monitored in real time, and a closed-loop dynamic tuning mechanism is constructed to continuously optimize control strategy parameters; step 5, dividing a control task into a group control layer, a region layer and a terminal layer, and establishing communication among the layers through a standardized interface so as to realize accurate transmission and execution of an instruction; and step 6, designing a friendly human-computer interaction interface, displaying a building operation state, energy consumption analysis and a control strategy, and supporting multi-terminal access and remote monitoring.

The invention provides an industrial air conditioner and waste heat recovery collaborative intelligent optimization control method and related equipment, and is applied to the technical field of data processing. Industrial air conditioner and waste heat recovery system data are fused through the Internet of Things and edge calculation, a standardized parameter sequence is generated through wavelet noise reduction and is converted into an energy flow network atlas, high-energy-consumption working conditions are subdivided through fuzzy clustering, and a dynamic energy efficiency coupling model is constructed. And based on the model, constructing an optimization equation by using an improved particle swarm algorithm, and obtaining the energy consumption of the key node in combination with an energy gradient utilization matrix. And an energy-saving potential coefficient is generated through the multi-dimensional feature matrix, a production plan grouping operation mode is combined, a self-adaptive optimization control model is constructed by using deep reinforcement learning, and finally a real-time regulation and control instruction and an energy efficiency scheme are generated to realize system collaborative optimization.

The invention discloses a multi-modal load prediction computing power center dynamic energy-saving scheduling method and system, and relates to the technical field of energy-saving scheduling, and the method comprises the following steps: executing long-tail fractal filtering based on service load data in a steady state for a long time in a computing power center, extracting a steady-state energy spectrum, and constructing a zero-drift contrast base plane; applying micro-amplitude random phase disturbance on the zero-drift contrast base plane, and enhancing weak disturbance characteristics in the steady-state energy spectrum in combination with an entropy gradient amplification strategy; and converting the enhanced precursor micro-fluctuation characteristics into a time sequence difference energy cloud picture. According to the method, zero-drift contrast base surface enhancement perturbation identification is constructed, and multi-scale energy analysis and Bayesian confidence correction are combined, so that accurate prediction and quantification of the burst load are realized; during surge triggering, a fractal slicing and low-power-consumption parallel scheduling strategy is adopted, high response is ensured, meanwhile, energy consumption is reduced, and the method is suitable for a computing power center scene with high performance and energy efficiency requirements.

The invention provides a server energy management method and device based on a BMC (Baseboard Management Controller). The method comprises the following steps: collecting hardware state data of a server in real time through the BMC; preprocessing the hardware state data, removing abnormal values and supplementing missing data to determine standardized hardware data; inputting the standardized hardware data into a load prediction model to determine a load prediction result; generating an energy efficiency evaluation report according to the standardized hardware data and the load prediction result; determining a dynamic energy adjustment strategy based on the energy efficiency evaluation report; and executing the dynamic energy adjustment strategy, and dynamically adjusting hardware operation parameters of the server so as to realize optimization of energy consumption. According to the method, the hardware control capability and the intelligent analysis algorithm of the BMC can be deeply fused, refined, automatic and intelligent management of server energy is realized, the energy utilization efficiency of a single server is remarkably improved, and conversion from extensive power supply to refined energy consumption management is realized.

The invention relates to the technical field of energy efficiency management of a super data center, and discloses a dynamic energy efficiency optimization and carbon traceability system for a super data center, which comprises the following steps of: acquiring energy consumption, an energy supply chain and environmental data of the super data center through an acquisition module, and performing fusion and time sequence processing to provide basic data for an analysis module; and the analysis module establishes a carbon footprint prediction model based on the time sequence data, calculates layered carbon emission, and constructs a multi-objective optimization model to generate an energy efficiency optimization strategy. And the central control module adjusts the operation of the equipment according to the strategy, compares the adjusted data with the prediction result, judges whether to correct the strategy, and realizes closed-loop optimization. And the display module visually presents the time sequence data and the optimization strategy, so that a closed-loop intelligent management mechanism of data acquisition, analysis and prediction, strategy generation and execution correction is realized, and the energy efficiency management precision and carbon emission controllability of the data center are effectively improved.

The invention provides an electric energy storage power station virtual inertia optimization method considering inertia space-time distribution characteristics, and the method comprises the steps: carrying out the virtual inertia optimization of an electric energy storage power station under the scene of considering the non-uniform inertia distribution of an electric power system under the high-proportion new energy access; constructing a source measurement frequency response characteristic model considering load power mapping and a load node frequency response characteristic model considering source measurement frequency mapping; the system small interference stability based on the characteristic root method is used as a frequency safety constraint condition; taking the minimum inertia and frequency modulation energy of the energy storage power station as a target, considering the physical constraint, the frequency security constraint and the system stability constraint of the energy storage power station, and proposing a network tracking / constructing energy storage power station inertia coordination optimization strategy considering the frequency dynamic space-time distribution; wherein the objective function is that the sum of the integral quantity of the load node frequency deviation and the inertia kinetic energy of the following / constructing network energy storage power station is minimum. According to the method, aiming at the characteristic of frequency space distribution differentiation in a novel power system, it is ensured that all nodes can meet double security constraints of frequency deviation and frequency change rate.

The invention discloses a sampling frequency energy-saving control method and system for a water quality monitoring sensor, and belongs to the field of water quality monitoring and self-adaptive control. The method comprises the steps of obtaining real-time water quality monitoring data and an energy consumption state, generating a water quality change trend prediction model, calculating a dynamic energy-saving sampling frequency based on the model and energy consumption, controlling a sensor to dynamically adjust the sampling frequency, and continuously monitoring, feeding back and updating the model. According to the method and system, a machine learning model is adopted to predict the water quality change trend, multi-objective optimization calculation of the dynamic energy-saving sampling frequency is achieved, the sampling frequency is smoothly adjusted through a progressive strategy, and the method and system have the pattern recognition and self-adaptive model updating capacity; on the premise of guaranteeing the monitoring precision, the sensor energy consumption and the data transmission cost can be remarkably reduced, and the intelligence, the stability and the battery life of the water quality monitoring system are effectively improved.

The invention provides an edge calculation optimization method for a seawater parameter monitoring Internet of Things system, and belongs to the technical field of edge calculation, and the method comprises the steps: constructing a seawater parameter monitoring sensor network, and calculating an energy consumption evaluation reference value based on a data change rate and a disorder degree; performing feature extraction on the seawater parameter data; deploying a double-model collaborative architecture comprising a terminal lightweight model and a center complete model; setting a dynamic energy consumption threshold judgment mechanism to allocate calculation tasks; executing lightweight edge calculation at the sensing nodes; transmitting the processed data through a low-power wide area network; the calculation center verifies and fuses the data; constructing a seawater parameter space-time distribution model; dynamically adjusting an energy consumption evaluation reference value by using a Pareto optimal solution set method; and periodically evaluating system energy consumption efficiency and updating model parameters. According to the method, dynamic balance of energy consumption and calculation efficiency of the seawater parameter monitoring system is realized, and the continuous operation capability and the monitoring reliability of the system are remarkably improved.

An elevator energy-saving control system of a direct feed network relates to the technical field of elevator energy-saving control, and comprises an energy conversion module for converting kinetic energy and gravitational potential energy generated during operation of an elevator car into direct current; the control system module monitors elevator running state data and power grid parameters in real time, judges whether the elevator is in a power generation state or not according to a monitoring result, automatically starts an energy feedback mode and controls starting and stopping of energy feedback and power adjustment; the inversion grid-connected module is used for converting the direct current into alternating current, processing the generated alternating current and feeding back the electric energy in the form of the processed alternating current to a power grid; and the man-machine interaction module is used for receiving data information of the control system in real time and converting the data information into visual display content, so that a worker can master the operation condition of the system in time and deal with abnormal problems. The elevator running energy consumption is remarkably reduced, the running cost is reduced, meanwhile, the service life of equipment is prolonged, the stability of the equipment is improved, and the influence on the environment is reduced.

The invention relates to the technical field of artificial intelligence and equipment control, and discloses an AI-driven commercial refrigerator dynamic energy-saving and load prediction system, which comprises the following steps: circularly multiplexing the working and shutdown of a compressor into an active detection process for the thermodynamic state of a refrigerator, and calculating the temperature of the refrigerator according to the temperature time sequence data generated in the process; according to the method, multi-scale dynamic characteristics capable of representing the total heat load and the heat exchange efficiency at the same time are extracted, a machine learning model is input for working condition classification, and then self-adaptive adjustment of a control strategy is achieved. The insight from the passive response to the single temperature to the core hidden variable of the dynamic thermal load is realized, so that the control system can identify the real working condition and make the optimal energy-saving decision, and the energy efficiency and the operation reliability of the equipment in the complex dynamic environment are improved.

The invention discloses a path planning method for a new energy logistics vehicle in a zero-carbon park, and the method comprises the following steps: generating an initial distribution path and a charging demand node based on the real-time remaining electric quantity, the cargo load capacity and a dynamic energy consumption prediction model of the new energy logistics vehicle; performing charging station priority optimization on the initial path according to the real-time occupancy state of the charging facility in the zero-carbon park and a vehicle remaining electric quantity threshold value, and generating a dynamic adjustable path set; based on historical distribution data, real-time traffic flow and weather condition input, performing congestion probability evaluation and energy consumption correction on the dynamic adjustable path set, and outputting a space-time optimal path scheme; and according to the logistics task allocation weight, the vehicle load capacity constraint and the cargo distribution characteristics, performing multi-target collaborative scheduling on the space-time optimal path scheme, generating a final path planning instruction, and updating a vehicle task queue in real time. According to the invention, new energy vehicle path dynamic optimization is realized, charging efficiency and transport capacity configuration are improved, and environmental adaptability is enhanced.

The invention discloses a dynamic energy-saving control method and system for a cold storage based on deep learning and multi-source data fusion, and relates to the technical field of cold storage energy-saving control, and the method comprises the steps: constructing a thermal field sensing network through a fixed sparse sensor array and a mobile thermal field scanning robot, and collecting temperature, humidity and thermal imaging data; reconstructing a three-dimensional thermal pixel field through registration, interpolation and other processing, and performing space-time alignment on the three-dimensional thermal pixel field, equipment parameters and inventory thermal physical properties; constructing digital twinborn deduction thermal field evolution based on a physical information neural network, and recognizing a high-temperature hot spot and a low-temperature safe area in combination with a space-time diagram attention network; the method comprises the following steps: by taking power consumption cost minimization and over-temperature risk controllability as targets, solving an optimal control strategy containing tuyere parameters and compressor frequency by using a multi-agent depth deterministic strategy gradient algorithm, driving equipment to execute targeted cold supply, and enabling a system to comprise a thermal field sensing layer, a data fusion layer, a digital twinning and prediction layer, a decision optimization layer and an execution layer. And accurate temperature control and energy conservation are realized.

The invention provides a dynamic energy-saving control method and device for garden landscape lighting, and the method comprises the steps: obtaining comprehensive monitoring data, such as multi-point environment illumination, people flow distribution and time period characteristics, and generating a partition lighting demand matrix through multi-dimensional fusion processing; identifying a contradictory region between the lighting demand and the energy consumption limitation, generating a conflict level mark based on energy efficiency analysis, and reversely deducing minimum power configuration; constructing a dynamic energy-saving constraint condition, carrying out boundary testing, generating a flexible energy-saving boundary, and determining an energy consumption control range; establishing a hierarchical control architecture of a main control layer and an auxiliary control layer, and generating a basic dimming instruction sequence and a supplementary control rule; analyzing an illumination influence relation of adjacent areas to establish a linkage mechanism, and optimizing operation parameters through cooperative control; the illumination power is dynamically adjusted by adopting an envelope modulation technology, a periodic fluctuation feature is extracted to identify an optimal energy efficiency mode, a self-adaptive control strategy is constructed, and intelligent dynamic energy-saving control of garden landscape illumination is realized.

The invention provides a novel multi-source information and damage coupled coal mine rock burst prediction method. The method comprises the following steps that rock mass fracture signals, a three-dimensional stress field and acoustic emission parameters are collected in real time through a micro-seismic monitoring array, an optical fiber stress sensor and a rock sound probe; an LSTM-attention fusion module is adopted to carry out spatio-temporal feature extraction on the collected multi-source data, an attention weight coefficient is generated, and a weighted fusion feature vector is output; calculating the rock mass damage degree based on a statistical damage mechanics Weibull distribution theory; based on the law of conservation of energy and the damage mechanics theory, a dynamic energy threshold function is constructed, the mining time effect and the rock mass damage state are incorporated into a quantitative evaluation system, and a dynamic energy accumulation rate threshold function is established by introducing a time accumulation effect and a damage coupling term; when the real-time energy accumulation rate is larger than or equal to the energy accumulation rate threshold value and the time derivative of the energy accumulation rate is larger than the sudden change threshold value, third-level early warning is triggered. According to the invention, advanced accurate early warning of the rock burst can be realized.

The invention discloses a leveling machine leveling precision self-calibration control system based on real-time displacement detection, and relates to the technical field of calibration data processing, and the system comprises a data collection and preprocessing module which is used for periodically collecting leveling monitoring data and preprocessing the collected data; the attitude reconstruction gap correction module is used for generating a theoretical gap of each node and correcting the theoretical gap; the deviation energy triggering judgment module is used for constructing a leveling deviation matrix, evaluating the dynamic energy level of the leveling deviation and judging whether a self-calibration process is triggered or not; and the fulcrum compensation calibration execution module is used for evaluating the adjustment force requirement of the leveling calibration action, generating a target height correction value, driving four fulcrums to synchronously adjust the attitude and completing the leveling attitude self-calibration closed loop after triggering the self-calibration process. The problems that in the existing thick plate leveling process, the posture of an upper roller is misaligned and cannot be quantitatively recognized in real time, leveling gap deviation is hidden and accumulated in multiple areas, and self-calibration triggering is not timely are solved.

The invention relates to an urban rail transit station ventilation and air conditioning system cooperative control method, device and equipment based on dynamic energy efficiency optimization and a medium. Constructing an energy efficiency model of the ventilation and air conditioning system by combining the energy efficiency curved surface characteristics of the water chilling unit and the cubic law power consumption characteristics of the frequency conversion equipment; an intelligent optimization algorithm is adopted, a dynamic scaling temperature feasible domain of chilled water of a water chilling unit and a frequency interaction limitation domain of frequency conversion equipment are constructed, the feasibility of a temperature set value is kept, meanwhile, coupling constraints in the frequency conversion equipment are decoupled, and an optimal parameter combination is solved; and the optimal parameter combination is issued to an execution layer, the actual energy efficiency ratio deviation of the system is monitored in real time, and energy efficiency model parameter re-calibration or iterative optimization is triggered according to a deviation threshold. Therefore, the overall energy efficiency of the ventilation air conditioning system is stably improved, and efficient operation under the dynamic working condition is achieved.

The invention discloses a dynamic energy storage scheduling method of a modularized photovoltaic system, and relates to the technical field of photovoltaic power generation system operation control, the method comprises the steps that a photovoltaic power generation system is divided into a plurality of modules with autonomous operation capability, each module comprises a photovoltaic power generation unit, an energy storage unit and a local control unit, the local control unit is used for collecting current illumination intensity, battery charge state, temperature, power grid power flow direction and load power data of the module in real time and periodically uploading the parameters to the system central coordination unit; the central coordination unit constructs a nonlinear load prediction model based on module-level load rate change, external temperature sensitivity and day and night periodic characteristics so as to improve the responsiveness to temporary sudden load change; according to the method, the problems of low operation efficiency, non-uniform energy storage utilization rate, inaccurate load prediction and scheduling response lag are solved.

The invention discloses an electronic detonator detonation dynamic regulation and control method and system based on performance monitoring, and relates to the technical field of electronic detonators. The method comprises the following steps: acquiring environmental stress parameters and detonator performance parameters in real time, and performing time sequence analysis to realize dynamic feature extraction; identifying an abnormal fluctuation time period, analyzing and extracting fluctuation characteristics, and quantifying the interaction influence of the environment and performance parameters; a performance degradation model is constructed based on voltage / resistance time sequence data, the degradation rate is dynamically calculated in combination with batch characteristics and acceleration test data, and the detonation failure probability is simulated and predicted; a detonation parameter safety interval is generated through reliability level mapping, and optimization design of trigger parameters and energy is achieved through timing compensation, discreteness grouping, network topology optimization and energy dynamic adjustment strategies; a closed-loop dynamic regulation and control strategy is formed, and high-reliability detonation of the electronic detonator under complex working conditions is guaranteed. According to the method, multi-dimensional data analysis and an intelligent optimization algorithm are fused, and the safety of blasting engineering and the system stability are effectively improved.

The invention relates to the technical field of motor servo control, and discloses a dual-motor servo press energy efficiency optimization method based on edge calculation. According to the method, operation parameters of a main driving motor and an auxiliary motor are collected in real time through edge calculation nodes deployed on a dual-motor servo press body; dynamically constructing a dual-motor cooperative load state matrix based on the operation parameters, and calculating a real-time comprehensive energy efficiency index by using an energy efficiency evaluation model in an edge calculation node; according to a deviation value between the index and a target energy efficiency threshold value, determining a dynamic energy efficiency adjustment parameter of the double motors; sending an instruction to the controller by the edge computing node, and synchronously updating a power distribution strategy; and monitoring and feedback adjustment are executed circularly until the dual-motor cooperative load state reaches a preset energy efficiency stable interval. According to the method, real-time monitoring and dynamic optimization of the dual-motor cooperative operation state are realized, and the energy efficiency stability of equipment operation is improved.

The invention aims to provide a dynamic energy optimization scheduling method and system for a temporarily-built optical storage system, and belongs to the technical field of temporarily-built optical storage system control, and the method comprises the steps: obtaining the photovoltaic power generation power, the energy storage charge state and the load demand in real time, judging the power balance state, and controlling the cooperative operation of each unit according to the power balance state. When photovoltaic power generation is sufficient, redundant electric energy is stored in the energy storage unit; if not enough, energy storage and electricity supplementation are preferentially carried out, and the diesel generator is started if not enough. The system also combines historical data and a neural network to predict a future scheduling strategy, and optimizes an energy storage charging and discharging plan. In addition, non-critical loads are identified through the intelligent electric meter, and the power supply priority is dynamically adjusted. The system improves the operation efficiency and stability of the optical storage system, prolongs the service life of equipment, and is suitable for temporary building facility power supply scenes.