727 results about "Energy consumption optimization" patented technology

The invention discloses a coal mine power supply intelligent monitoring system based on the Internet of Things, belongs to the field of coal mine power supply monitoring, and aims to solve the problems that an existing coal mine power supply intelligent monitoring system is lagged in response, high in false alarm rate and large in manual dependence degree. According to the invention, through the end-side global sensing module, the data advanced analysis module, the edge data processing module, the data transmission module, the cloud data analysis and model construction module and the fault early warning and closed-loop control module, the real-time acquisition of the equipment state is realized by deploying multiple types of intelligent sensors; local data preprocessing and abnormal pre-judgment are carried out by combining edge computing nodes, an equipment health degree model is established by adopting a time sequence data association analysis algorithm, closed-loop control of overload prediction, electric leakage positioning and energy consumption optimization is realized through multi-source data fusion analysis, and finally a three-level intelligent monitoring system of end side sensing-edge computing-cloud decision is formed. The system response efficiency and accuracy are improved, and the personal labor intensity is reduced.

The invention discloses a network card data local preprocessing system fused with edge computing, and relates to the technical field of edge computing and artificial intelligence collaborative optimization. Comprising an edge computing unit, a hierarchical collaborative architecture, a model hot switching and generative fragmentation module, an intention recognition and adaptive scheduling module, a delay energy consumption optimization scheduling module, a CXL zero-copy sharing module, an edge computing unit integrated processor, an FPGA or ASIC and a neuromorphic computing unit. According to the method, an FPGA, an ASIC and a neuromorphic computing unit are integrated in an intelligent network card, microsecond-level dynamic connection reconfiguration and adaptive generative model fragmentation execution are realized through a reconfigurable Mesh interconnection matrix, an attention layer and a feed-forward layer of a Transform class model are fragmented and allocated to different computing units for parallel execution, and cross-card streamlined processing is realized in cooperation with a zero-copy shared memory. And the intention recognition module is deeply coupled with the model hot switching module, so that dynamic model switching and fragmentation strategy optimization based on service priorities and system loads are realized.

The invention discloses an information processing method and system based on the Internet of Things, and relates to the technical field of Internet of Things services, and the method comprises the steps: dynamically obtaining the computing power, energy consumption and network state of each node through an environment perception module, and constructing a node capability vector and a link performance vector; the task analysis module is used for carrying out structured modeling on tasks and identifying candidate segmentation points and unloading targets; the cost modeling module establishes a joint cost function of time delay and energy consumption; the strategy generation module generates an optimal unloading strategy based on multi-agent reinforcement learning; and the scheduling communication module executes task unloading and result integration according to the strategy, and feeds back execution data to update the system state. According to the method, intelligent, low-delay and energy-consumption optimization processing of task segmentation unloading is realized, and the method is suitable for a dynamic heterogeneous Internet of Things computing environment.

The invention relates to the technical field of computing power-energy consumption optimization, in particular to an edge AI reasoning computing power-power consumption cooperative system, which comprises a task decomposition module for decomposing a complete reasoning task into n independent sub-tasks, n being greater than or equal to 1; the task analysis module is used for extracting feature parameters of the subtasks and generating task feature vectors; and the computing power prediction module inputs the obtained sub-task feature vectors and different computing unit type labels into a pre-constructed task computing power demand prediction model, and predicts the computing power demands of the sub-tasks on different computing units. In the scheme construction and task allocation stage, the system carries out candidate calculation unit screening and combination generation based on a multi-scheme calculation power energy consumption matrix, calculates the total energy consumption and time delay matching degree of each combination, and selects an execution scheme with the lowest energy consumption under the real-time condition from the total energy consumption and time delay matching degree. The mechanism enables each subtask to be flexibly migrated between different types of computing units.

The invention discloses a mobile phone energy consumption optimization method, and relates to the technical field of information, and the method comprises the steps: obtaining a current game frame of a mobile phone game application, and extracting a key rendering parameter of the current game frame; according to the current game frame and the historical game frame, calculating a static scene element space consistency score and a dynamic rendering object state change rate; according to the static scene element space consistency score and the dynamic rendering object state change rate, generating a computing resource allocation scheme; according to the computing resource allocation scheme, GPU rendering pipelines and CPU computing resources are configured, corresponding game computing execution paths are selected, and the computing execution paths comprise a complete rendering pipeline path, an incremental rendering path or an inter-frame interpolation path; aiming at high energy consumption of 3D scene rendering in a mobile game, a three-level rendering strategy selection mechanism based on scene change degree and the like are established, and the energy consumption of a mobile phone is reduced while the game quality is kept.

The invention relates to the technical field of automobile energy supply, in particular to a solid hydrogen storage-fuel cell integrated power assembly system based on dual-mode hydrogen supply, which comprises a hydrogen supply path identification module, a hydrogen storage unit state monitoring module, a linkage path switching module, a fuel cell load matching module and a safe hydrogen discharge regulation and control module. According to the method, the hydrogen supply pressure difference value is obtained by dynamically comparing the pressure of the cavity of the hydrogen storage tank with the pressure of the hydrogen inlet of the fuel cell, the current hydrogen supply path state can be accurately recognized in the early stage, rapid judgment of the hydrogen supply mode and accurate control of path switching are achieved, whether the risk of hydrogen supply interruption exists or not can be effectively screened, and the reliability of hydrogen supply is improved. The switching reliability of a hydrogen supply channel is improved through cross comparison of key state signals of a main channel and a standby channel, and instant response and energy consumption optimization of hydrogen discharge safety regulation and control are guaranteed through a linkage feedback mechanism between the hydrogen concentration change rate in unit time and catalytic converter control power.

The invention discloses an agricultural heterogeneous multi-core SoC (System on Chip) architecture and equipment integrating vision and environmental perception. According to the architecture, a heterogeneous multi-core design is adopted, a universal CPU and an AI acceleration and preprocessing module are integrated on an on-chip system, an efficient task scheduling mechanism is combined, real-time management and control of multi-source heterogeneous tasks such as image processing, radar resolving and path planning are achieved, and the system response speed and the task execution efficiency are improved. In the aspects of communication and storage, NoC and AXI buses are adopted to cooperatively construct a high-speed data path, DDR4 and Flash mixed storage is supported, and the transmission stability of large-bandwidth data is guaranteed. The power supply and clock management module realizes modularized start and stop and energy consumption optimization through a multi-voltage domain and dynamic power consumption control technology, and is suitable for outdoor long-time operation. The IO and peripheral interface module provides abundant interfaces, and system expansibility and deployment flexibility are improved. The overall framework integrates the advantages of high integration level, high adaptability and low power consumption, and a solid calculation basis is provided for agricultural intelligent equipment.

The invention provides an aluminum product rolling process real-time monitoring and optimizing system based on digital twinning, and relates to the technical field of power battery aluminum foil rolling. The monitoring and optimization system comprises a data acquisition module, a digital twin modeling module, a real-time monitoring module, a dynamic optimization module, a feedback control module, a green manufacturing optimization module and a cross-alloy adaptive module. According to the aluminum product rolling process real-time monitoring and optimizing system based on digital twinning, the data acquisition module, the digital twinning modeling module, the real-time monitoring module, the dynamic optimizing module, the feedback control module, the green manufacturing optimizing module and cross-alloy self-adaption work cooperatively; the defects in the aspects of data acquisition and integration, process parameter optimization and anomaly detection, energy consumption optimization and green manufacturing in the prior art are overcome.

The invention belongs to the technical field of energy consumption optimization, and particularly relates to a digital twin building energy consumption dynamic optimization system, which comprises a modeling module for acquiring physical, structural and equipment layout parameters of a building, and constructing a target building three-dimensional region segmentation model containing geometric transmission constraints by combining three-dimensional modeling and a grid segmentation algorithm; the control mapping module is used for collecting data such as regional people flow density and equipment energy consumption, and constructing multi-level association control mapping including people flow-rate first association mapping, rate-energy consumption second association mapping and comfort level score-based second association feedback mapping through an association analysis algorithm; the analogue simulation module constructs a generator and a discriminator through association mapping, performs synchronous confrontation simulation in combination with a three-dimensional region segmentation model, and solves an optimal energy consumption rate; and the feedback adjustment module compares actual operation with feedback data, corrects mapping parameters and optimizes algorithm weights, realizes building energy consumption dynamic optimization, and ensures that the overall energy consumption and comfort level score meet preset thresholds.

The invention relates to the field of intelligent manufacturing, in particular to a papermaking process intelligent parameter adjustment method and system oriented to energy consumption optimization, and the method comprises the following steps: S1, obtaining a historical production and energy consumption report, a product quality standard, a productivity plan, an equipment capability curve and safety red line, and an energy price and carbon emission coefficient, and defining a multi-objective optimization function and constraint; s2, acquiring production process data, and preprocessing the data to obtain time sequence sample data; s3, performing feature construction based on the time sequence sample data to obtain a feature set, and constructing a process-energy consumption directed acyclic graph by adopting NOTEARS to obtain a causal atlas; s4, constructing a short-term energy consumption prediction model and a quality prediction model, and performing short-term energy consumption prediction and quality prediction based on the feature set, the causal atlas, the target KPI and the constraint; and S5, performing multi-objective strategy optimization according to the objective function and constraint, short-term energy consumption prediction and quality prediction results. On the premise of ensuring the product quality and the production safety, the energy consumption of a unit product can be effectively reduced.

The invention relates to the technical field of cabin environment regulation and control, and discloses an immersive intelligent cabin environment regulation and control and energy consumption optimization system. The system multi-source sensing module is used for collecting environment multi-modal data streams through various sensors distributed inside and outside a cabin, and performing time alignment and space registration on the data to generate an environment data set; the dynamic graph generation module is used for performing multi-level feature analysis on the environment data set, extracting environment feature representation, constructing a parameter dependency graph and fusing time sequence dynamic information to generate an environment evolution graph; the core element extraction module is used for deriving an element vector of each dimension from the environment feature representation, calculating an element fluctuation index of each dimension, and screening out a core element sequence in combination with a topological relation in an environment evolution graph; and the intelligent decision-making module is used for generating an optimization control strategy according to the core element sequence and realizing self-adaptive adjustment and energy consumption management of the cabin environment.

The invention discloses a liquid cooling energy storage power station intelligent temperature control system based on DNN and fuzzy logic, and relates to the technical field of energy storage. Comprising a data acquisition and multi-dimensional preprocessing module used for acquiring liquid cooling energy storage multi-source working condition data in real time, performing feature extraction and performing data preprocessing; the DNN depth temperature prediction module is used for constructing a DNN depth temperature prediction model, predicting the temperature of the battery pack, identifying an overtemperature risk and taking safety guarantee measures; the fuzzy logic intelligent decision module is used for predicting the flow of the cooling liquid and implementing a temperature control regulation decision; and the execution and feedback self-optimization closed loop module is used for correcting the flow of the cooling liquid and realizing a self-learning evolution closed loop of temperature control regulation. The problems that an existing liquid cooling energy storage power station temperature control system is poor in non-linear and dynamic change adaptability, insufficient in temperature control precision and response speed and poor in energy consumption optimization, complex heat management requirements cannot be met, and then comprehensive operation performance is reduced are solved.

The invention discloses an industrial equipment energy consumption optimization method based on sensor data and a fuzzy neural network, and belongs to the technical field of industrial automation control, and the method comprises the specific steps: S1, collecting process data including workpiece temperature and equipment power in real time through sensors disposed on industrial equipment and a workpiece, a state estimation algorithm based on a Kalman filtering theory is adopted to carry out online calibration on key state variables of a pre-constructed multi-physics field coupling digital twin model so as to generate a system state vector capable of accurately reflecting high-dimensional information such as a predicted temperature and a curing degree in a workpiece; the method ensures the long-term effectiveness and accuracy of the digital twinborn model, and can reduce the comprehensive energy consumption while obviously reducing the residual stress of the product and shortening the curing period.

The invention relates to a multi-task-point cruise path planning method and device for an unmanned aerial vehicle. The method comprises the following steps: acquiring flight data of the unmanned aerial vehicle in multiple flight stages; the flight data comprises corner data when the flight stage is a corner stage; constructing a flight energy consumption model according to the undirected graph, the flight data and the rotation angle data; the energy consumption of each task point in the cruising process of the unmanned aerial vehicle is obtained; performing iterative optimization on the energy consumption of the plurality of task points based on a wild dog optimization algorithm to obtain a path planning result, and controlling the unmanned aerial vehicle to fly according to the path planning result; energy consumption of different flight stages is quantified by constructing a flight energy consumption model, including energy consumption of rotation angle data in a rotation angle stage, so that the complex environment adaptability is improved, iterative optimization is performed on a path in combination with a wild dog optimization algorithm, the problems of inaccurate energy consumption modeling and easy falling into local optimum are solved, and the energy consumption modeling efficiency is improved. The method has the advantages that the energy consumption optimization efficiency and accuracy of path planning are improved, and the local optimum problem is avoided.

The invention relates to a large model energy consumption optimization method and device, computer equipment, a computer readable storage medium and a computer program product. Comprising the steps of collecting hardware state data and task data of a large model; performing stage identification according to the task data, and determining a current stage; performing state prediction according to the hardware state data and the task data through a prediction model corresponding to the current stage to obtain target state information; generating optimization parameters of the current stage through a joint optimizer according to the target state information; and adjusting the resources of the large model according to the optimization parameters. In combination with stage perception and dynamic resource adjustment, a differentiated resource adjustment strategy based on different stages is realized, specific energy consumption pain points in an AI scene are solved, high power consumption of large model training, instantaneous fluctuation of reasoning requests and the like are reduced, sustainable and efficient operation of an AI system is ensured, the computing power demand and resource consumption of a large model are balanced, and the system performance is improved. And wide landing and development of a large model in various scenes are facilitated.

The invention discloses an energy consumption optimization management method and system for compressed air of an energy storage aquifer, and relates to the field of energy consumption optimization management.The method comprises the steps that an initial adjustment frequency threshold value is set by dynamically selecting regulation and control parameters, the adjustment effect is verified through a model, and equipment maintenance or replacement is automatically triggered when the threshold value is exceeded; the timeliness and the accuracy of exception processing are obviously improved; further setting a monitoring period, circularly executing data acquisition and parameter adjustment, combining real-time monitoring of maintenance cost and working time length data, dynamically optimizing an adjustment strategy and recording historical data when a threshold value is exceeded, and finally constructing a total maintenance cost-working time length mapping model to realize continuous optimization; besides, by introducing a second threshold maximum adjustment frequency and a sensor redundancy mechanism, when the adjustment does not reach the standard, a standby sensor is automatically added, and the optimization process is repeated, so that the reliability of data acquisition and the stability of the system are effectively enhanced.

The invention relates to an unmanned aerial vehicle track optimization method, system and device in an urban high-density airspace and a medium, and the method comprises the steps: dynamically dividing the urban airspace into space-time four-dimensional grid units of different scales according to the vertical height and the time axis based on the real-time airspace traffic density and environmental conditions; establishing a guide vector field pointing to a target position in each four-dimensional grid unit, and when an obstacle and / or a flight conflict is detected, generating a local disturbance vector and dynamically correcting the guide vector field in real time so as to autonomously generate a candidate flight trajectory; and calculating the energy consumption of the candidate flight path based on an unmanned aerial vehicle dynamics model and an environment model, and selecting a path with the lowest total energy consumption as a final flight path under the condition of meeting safety constraints and task time requirements. Compared with the prior art, in a complex dynamic environment of an urban high-density airspace, unification of real-time efficient planning of the flight path of the unmanned aerial vehicle, safe obstacle avoidance and low-energy-consumption optimization is realized.

The invention provides a multi-energy coupling carbon effect optimization method and system based on a large time sequence model, belongs to the field of data processing, and is used for realizing systematic collaborative optimization capability. The method comprises the following steps that: access point equipment acquires multi-dimensional carbon energy consumption data of a target service requester from equipment of the target service requester, and the equipment of the target service requester is accessed to the access point equipment; the access point device preprocesses the multi-dimensional carbon energy consumption data through a front-end model of the time sequence large model to obtain intermediate carbon energy consumption data, and sends the intermediate carbon energy consumption data to the application device; the front-end model is deployed at the local of access point equipment, and the intermediate carbon energy consumption data is non-semantic data and cannot represent information of a target service requester; and the application device analyzes and processes the intermediate carbon energy consumption data through a back-end model of the time sequence large model to obtain an analysis result, the back-end model is deployed locally in the application device, and the analysis result indicates a carbon energy consumption optimization suggestion for the target service requester.

The invention discloses an underwater gliding robot anchoring process energy consumption optimization method and system based on a subdomain grey box model. The energy consumption optimization method comprises the steps that a white box energy consumption mechanism model in the underwater gliding robot anchoring process is established; the whole anchoring energy consumption stage of the underwater gliding robot is divided into a plurality of sub-domains, and a Latin hypercube design method is adopted to simulate and generate a plurality of sub-domain samples meeting variable range constraints by using a hardware-in-the-loop simulation platform; selecting a Kriging model as a sub-domain agent model, fusing the white-box energy consumption mechanism model and constraint conditions of an oil bag volume and a movable mass position to form a sub-domain grey-box model, and performing segmented energy consumption fitting by using the sub-domain grey-box model; and with minimization of energy consumption fitted by the sub-domain grey box model as a target, performing iterative optimization on the sub-domain proxy model by adopting a dynamic guidance self-adjusting particle swarm algorithm, and outputting optimal planning parameters. The method realizes anchoring full-process low-energy-consumption control, and is suitable for marine resource exploration, hydrological monitoring and other tasks.

The invention relates to the technical field of ship energy efficiency management, and discloses an energy efficiency improvement method and system based on course-speed-trim collaborative optimization, which constructs a course feature code based on navigation environment forecast information and electronic chart information, and combines real-time state information and decision variables of a ship to improve the energy efficiency of the ship. Generating voyage number accumulated energy consumption expectation by using a ship energy consumption prediction model fused by physical information; taking expectation minimization as a target, coupling navigation time constraints, host working condition constraints and ship stability safety boundaries, constructing a three-variable collaborative optimization model of the route path, the planned navigational speed of each route segment and the planned trim value, solving the model, and outputting a collaborative optimization scheme; and on-line parameter adaptive updating is performed on the model based on real-time data during navigation, and dynamic re-planning is triggered. The global voyage number energy consumption optimization is realized through a three-variable collaborative optimization framework and physical information fusion prediction, and the adaptability and robustness of an optimization scheme in a dynamic environment are improved by means of an online learning and rolling optimization mechanism.

The invention relates to the field of building energy automation control, and discloses a building energy consumption optimization method based on artificial intelligence, which comprises the following steps: acquiring an energy loop state, generating a reference control instruction, superposing a detection signal of which the amplitude is lower than the upper limit of a mechanical dead zone, and sending the superposed detection signal to an execution mechanism; extracting a phase difference of the feedback response relative to the detection signal by using orthogonal correlation logic; according to the matching relation between the instruction gradient direction and the phase difference curvature evolution trend, an asymmetric damping rule is configured for a loop, and control parameters are corrected, the asymmetric physical characteristics of building thermal inertia are matched, phase mismatch between the instruction and physical response is eliminated, energy backflow and overshoot oscillation generated in the adjusting process are restrained, and the stability of the building thermal inertia is improved. Therefore, energy consumption adjustment is converted from oscillation approximation to smooth cut-in, and on-line identification of the deterioration state of the actuating mechanism is realized on the premise that hardware cost is not increased.

The invention provides an intelligent wire harness adaptive control method and system, and the method comprises the steps: S2, extracting a corresponding current range and a communication protocol from an output mode library according to an equipment type judgment result, and determining an initial output parameter; s4, executing the adjusted output parameters through a low-power-consumption communication module, obtaining equipment operation state data, and judging whether the real-time operation requirement is met or not; s6, after the pre-adjustment parameters are executed, equipment response data and energy consumption data are obtained, and whether system overload or power consumption exceeding exists or not is judged; s8, updating the equipment feature database and the output mode library according to the adjusted operation configuration, and obtaining updated collaborative performance indexes and energy consumption indexes; and S9, if the collaborative performance index and the energy consumption index do not reach the performance threshold, repeatedly executing the combined optimization process of the dynamic adjustment algorithm and the feature analysis algorithm. According to the invention, high-efficiency cooperation and energy consumption optimization of the wire harness and the intelligent equipment are realized.

The invention discloses a security storage chip energy consumption optimization method based on multi-objective optimization, and relates to the technical field of energy consumption optimization, and the method comprises the steps: inputting the threshold voltage distribution characteristics of a security storage chip of a candidate parameter set into an aging model, fitting the rule of the threshold voltage distribution characteristics along with the time under an accelerated aging condition, and obtaining an aging model; aging model parameters are obtained; calculating the data retention failure rate of the security storage chip in the target life cycle by using the aging model parameters, and obtaining an extended feasible region of the instant erasure security constraint and the long-term retention security constraint in combination with a data retention judgment standard; and establishing a multi-objective optimization function with reduction of operation energy consumption and maintenance energy consumption as optimization objectives in the extended feasible region, and obtaining a candidate solution set through a multi-objective optimization algorithm. According to the method, data retention prediction in the life cycle is realized, and energy consumption optimization and long-term reliability constraint are combined.

The invention discloses a task scheduling and energy consumption optimization system and method based on distributed satellite calculation, and the method comprises the following steps: obtaining the floating point calculation amount and data size of each subtask, obtaining the calculation power and link rate of each satellite, and calculating the estimated execution time of the subtasks on the satellites according to an execution time formula; further obtaining the calculation power, the communication power and the corresponding duration of each satellite, and estimating the total energy consumption through an energy consumption formula; and optimizing a task allocation scheme based on the energy consumption evaluation, thereby reducing the overall energy consumption of the satellite cluster.

The invention relates to the technical field of hydraulic support intelligent supporting scheme design, in particular to a hydraulic support intelligent supporting method and system and electronic equipment. The method comprises the following steps: collecting roof pressure, bracket attitude and geological structure parameters in real time through a multi-modal sensor array; performing spatio-temporal feature fusion by utilizing the edge computing nodes to generate a three-dimensional support state map; constructing a dynamic weight adjustment strategy based on a reinforcement learning algorithm, and generating multi-objective optimization support parameters; a control instruction is issued through a dual-mode communication network to realize self-adaptive adjustment of the supporting strength; and a closed-loop feedback verification mechanism is established in combination with the laser radar scanning roof sinkage. And a digital twin model and an energy consumption optimization function are integrated. The problems that a traditional method is single in data dimension, rigid in decision and insufficient in reliability are solved, dynamic optimization of support parameters, active prevention and control of risks and collaborative improvement of energy efficiency are achieved, and the support safety and the intelligent level under complex working conditions are remarkably improved.

The invention discloses an automobile fastener production energy consumption optimization control method and system, and relates to the technical field of fastener production, and the method comprises the following steps: obtaining a real-time current waveform signal and a power grid fundamental wave voltage signal of an inversion unit in an energy feedback link, building a phase dynamic drift model through a time difference analysis algorithm, and carrying out the calculation of the phase dynamic drift model; continuous mapping of energy flow direction change is formed on a time axis; and based on the established phase dynamic drift model, extracting an offset rate continuous change section between the current waveform and the power grid fundamental wave, constructing a phase trend prediction function by using a sliding window algorithm, and converting the time characteristic of the energy flow direction change into a phase prediction signal trajectory. Through phase dynamic drift modeling and lead compensation control, synchronization of feedback current and power grid voltage is realized, and ripple current and element stress are reduced; and through energy density change rate extraction and dynamic filtering regulation and control, the energy flow is stable and continuous, and efficient and energy-saving control over fastener production is achieved.

The invention provides a solid state disk energy consumption optimization method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the throttling operation of the transmission bandwidth of a PCIe link if the first data transmission rate of the PCIe link does not meet the transmission rate requirement; performing layering operation on the cache space of the solid state disk based on the link transmission state of the throttled PCIe link to obtain a layered cache structure; determining a migratable task in each cache layer based on a data storage condition of each cache layer in the layered cache structure and a task execution condition of the solid state disk, and migrating the migratable task of each cache layer to a target cache region to obtain a cache layout after task migration; and if the second data transmission rate of the PCIe link after throttling meets the transmission rate requirement, adjusting the working mode of each component in the solid state disk based on the second data transmission rate and the cache layout. The energy consumption of the solid state disk is optimized, the read-write performance is improved, and the service life is prolonged.

The invention relates to the technical field of data reasoning, and discloses an intelligent monitoring and optimizing method and system for energy consumption in the solid waste treatment process, and the method comprises the steps: cleaning noisy points of energy consumption data during solid waste treatment, and obtaining standard energy consumption data of solid waste treatment; extracting characteristic indexes of time sequence characteristics in the standardized energy consumption data; according to the characteristic indexes, the energy consumption abnormal time period and the high energy consumption link of the target solid waste treatment process are recognized; according to the operation state data of the high-energy-consumption link, state association is carried out on the energy consumption abnormal time period, and an energy efficiency association map of the solid waste treatment process is obtained; according to the energy efficiency correlation map, performing energy consumption optimization on the target solid waste treatment process to obtain an optimized regulation and control strategy of solid waste treatment; performing energy level supervision on the target solid waste treatment process according to the optimized regulation and control strategy; according to the invention, the efficiency of intelligent monitoring and optimization of energy consumption in the solid waste treatment process can be improved.

The invention provides an energy consumption optimization method and system for mine ventilation, relates to the technical field of mine ventilation, and solves the technical problems that an existing mine ventilation system is too high in energy consumption and uneven in air distribution. The method comprises the following steps: acquiring environment data in real time through a sensor network; simulating air volume distribution in a mine by using a digital twinborn model, and inputting the environmental data into a space-time diagram convolutional network to predict a ventilation demand; performing global optimization by adopting a dynamic weight multi-objective optimization algorithm according to the air volume distribution and the ventilation demand, and generating a multi-fan cooperative control instruction; the control instruction is issued to a frequency converter and a PLC, fan rotating speed adjustment is executed, and a fault diagnosis mechanism is integrated to achieve redundancy switching; and feeding back the execution effect to the digital twinborn model for iterative optimization. The method is used for ventilation system design, operation and maintenance links in various underground mineral exploitation scenes.

The invention relates to the field of digital twin driving, and discloses an electric actuator energy consumption optimization method and system based on digital twin driving, and the method comprises the steps: collecting the current, torque, rotating speed, temperature, vibration and environment wind speed data of an electric actuator in real time, carrying out the online recognition of the thermal-friction coupling parameters of the actuator through a multi-scale sliding window method, and carrying out the online recognition of the thermal-friction coupling parameters of the actuator; the operation state of the actuator is dynamically updated in combination with the load damping change and the long-term health condition; the load evolution trajectory of the actuator is extracted, transient fluctuation and long-term degradation trend analysis are combined, and the energy consumption deviation and the performance bottleneck existing in the operation process of the actuator are recognized; according to the method, related actuator components and mutual influences thereof are comprehensively analyzed, parameters, feature weights and updating frequencies of the digital twin model are dynamically adjusted, and a real-time energy consumption optimization control strategy is generated. The method has the advantage that the real-time response capability and the long-term self-adaptive precision are improved.