2122 results about "Total energy" patented technology

The invention provides a BIM-based building energy consumption simulation analysis optimization method and system, and relates to the technical field of building energy consumption, and the method comprises the steps: building a building energy consumption digital twinborn model through integrating a building information model (BIM) and real-time operation data, such as indoor and outdoor temperature, equipment operation, electrical load and personnel density, collected by an Internet of Things sensor. An LSTM and GNN mixed deep learning algorithm is used to predict building energy consumption, and clustering analysis is used to identify an energy consumption abnormal mode and a peak period. And finally, based on a reinforcement learning algorithm, constructing an energy consumption optimization model taking total energy consumption minimization as a target and taking indoor thermal comfort and air quality as constraints, outputting equipment optimization operation parameters such as air conditioner temperature, fresh air volume, illumination brightness and the like, and realizing intelligent optimization control of building energy consumption, so that the building energy consumption is reduced, and the building operation efficiency is improved.

This application discloses a method, system, and related equipment for intelligent energy management of new energy vehicles. Based on the vehicle's starting and ending points, at least one candidate energy-saving path is determined. The predicted energy consumption of the vehicle along at least one candidate energy-saving path is lower than that of other paths. The total energy consumption is predicted based on road condition information and energy consumption impact information for each path. In response to the selection of at least one candidate energy-saving path, a preset travel route is determined. The preset travel route includes multiple road segments, and the total energy consumption includes the energy consumption of each road segment. With the goal of minimizing fuel consumption along the preset travel route, the engine's operating state is controlled based on the initial state of charge (SOC) of the power battery in each road segment, the energy consumption of the road segment, and the vehicle's actual overall demand, ensuring the engine operates within its high-efficiency range. Using this application can reduce fuel consumption for users and improve the driving experience.

The invention discloses an intelligent job shop scheduling method. The method comprises the following steps: S1, establishing a personalized improved NSGA-II algorithm module and a DQN-based adaptive decision algorithm module; s2, establishing a mathematical model taking the maximum completion time, the total load of equipment and the total energy consumption of a workshop as optimization targets; s3, in a personalized improved NSGA-II algorithm module, considering a workshop optimization target, adopting multiple strategies for initialization, and adding a user personalized demand to screen a target population; s4, in a DQN-based adaptive decision algorithm module, population features are extracted to construct an agent state space, and a deep Q network experience playback mechanism is utilized to train the relationship between agent capture scheduling information and a scheduling target to perform adaptive action decision; and S5, returning the optimal action predicted by the DQN model to the NSGA-II, adjusting the optimal parameter proportion according to the real-time state, and improving the multi-target solution quality.

A method for coordinating deployment of unmanned aerial vehicles (UAVs) within a designated region involves obtaining constraint data that specifies the operational limitations under which the UAVs are allowed to operate. A cost function is defined, having a set of cost terms corresponding to these constraints, including a term for the energy consumption of each UAV. This energy consumption is the power required by each UAV to travel from a source to a destination along a prescribed flight path. The method includes executing a UAV-capacity maximization function that generates flight paths for the UAVs based on the cost function, which adjusts the flight paths to minimize the total energy consumed while ensuring that the UAVs do not breach the operational constraints.

The invention relates to the technical field of heat dissipation control, and discloses a heat dissipation fan multi-order filtering control method and device, and the method comprises the steps: collecting the operation parameters of a multi-fan heat dissipation system, and carrying out the construction of a dynamic state space model, and obtaining a system operation state equation and a temperature observation equation; inputting the system operation state equation and the temperature observation equation into an expectation maximization sliding window iteration extended Kalman filter to obtain a system state estimation value and a prediction error covariance matrix; performing three-stage series design on the cut-off frequency of the low-pass filter, the passband range of the band-pass filter and the coefficient of the adaptive filter to obtain a system multi-order filtering control model; based on a system multi-order filtering control model, an airflow coupling influence coefficient matrix among multiple fans is calculated, the total energy consumption, the noise level and the heat dissipation efficiency are subjected to optimization solution, a fan control optimization parameter set is obtained, the heat dissipation efficiency is guaranteed while the energy consumption is reduced, and dynamic balance among all control units is achieved.

The invention provides an inter-fleet dynamic electric quantity balance and collaborative replenishment system, which relates to the technical field of shipping transportation, and comprises an intelligent scheduling module used for obtaining a data set containing electric power conditions, navigation parameters, sea condition influences and task progress requirements of each ship based on ship motion states, ship equipment is monitored in real time to obtain data and analyze environmental influence to obtain adjustment parameters, then the total energy consumption contribution of a single ship is calculated, the total loss of a fleet is quantified to fuse to obtain the initial amount of total energy consumption and correct to obtain the navigation efficiency, and then a power output adjustment strategy is formulated for a main propulsion ship according to the navigation efficiency and an electric power balance target. And finally, a collaborative supply path is planned by taking the low-power ship as a core and combining multi-party information. According to the invention, efficient coordination of deployment and supply of the fleet on power management is realized, the transportation efficiency is improved, the cost is reduced, and the navigation safety is enhanced.

The embodiment of the invention relates to the field of data processing, in particular to a multi-market transaction prediction method and system for a virtual power plant. The method comprises the steps of obtaining first predicted output data based on historical electricity prices, user load data and time-of-use electricity prices before a transaction, obtaining a first preset total energy demand through correction of a load fuzzy response model, and generating a day-ahead market bidding strategy and a first bidding electricity price; and on the same day of the transaction, acquiring second predicted output data based on the real-time data, receiving the first bidding result, correcting the first bidding result to obtain a second preset total energy demand, and generating a real-time market bidding strategy and a second bidding electricity price. And after the second bidding result is received, dynamically adjusting the real-time preset total energy demand, optimizing the power scheduling and charging and discharging strategy of the energy storage system, and balancing the energy reserve after the transaction. According to the method, the bounded rationality problem of the controllable load in the demand response process is solved, the user demand response result and the DER are effectively combined, and a solution is provided for the VPP to participate in the bidding plan of multiple types of power markets.

The invention discloses a park energy storage intelligent scheduling system and method based on big data, and relates to the technical field of big data park energy storage intelligent scheduling, and the method comprises the following steps: S100, collecting the real-time operation data of multiple types of energy systems in a park, constructing a time sequence database, and carrying out the preprocessing; s200, predicting load change based on an energy demand scene, and constructing a polymorphic energy storage model in combination with dynamic characteristics of energy storage equipment; s300, defining a multi-objective optimization function taking energy loss, renewable energy utilization rate and energy storage efficiency as cores, establishing a total energy balance model, and setting constraint conditions of charging and discharging and heat and cold storage power of energy storage equipment; and S400, updating the load demand and the energy storage state by using the real-time data, calculating the load deviation and correcting the target function, and readjusting the charging and discharging of the energy storage equipment and the heat storage and cold storage power distribution according to the corrected target function.

The invention relates to a neuromuscular electrical stimulation multi-objective self-tuning optimization method, which comprises the following steps: dynamically establishing a framework for muscle response in a time window sliding modeling mode, and describing short-term excitability change and fatigue accumulation by the framework according to historical stimulation frequency density and total energy integral accumulation variable; introducing a short-time response curve recognition mechanism: after stimulation, according to micro changes of an electromyogram and a force output curve, judging whether a nerve and muscle coupling state is in a deviation trend or not; when the output trend is evolved towards the target improvement direction, current strategy fine tuning is kept; if the trend is stagnated or reversed, the weight combination or the step length is actively adjusted; constructing a multi-target co-integration matrix for analyzing whether the current response changes of different targets are in a consistent direction; when it is detected that two certain targets have an inverse correlation change, the system carries out conflict decomposition processing once; interference compromising is carried out on conflict indexes, and short-term power down-regulation is carried out; and forming a dynamic strategy framework.

The invention relates to the technical field of screening, in particular to a dynamic sorting method and system based on multistage screening and airflow compensation. The method comprises the steps that 1, space phase screening pretreatment is conducted, specifically, in a rotary type multidirectional screening channel, white rice is made to rotate through a flow guide structure, the included angle between the long axis direction of rice grains and the long edge of a screening hole is obtained in real time, and the rotating angular speed of a screen is dynamically adjusted according to the included angle; 2, mass-airflow coupling sorting is carried out; step 3, performing deformation feedback optical sorting; 4, energy field cooperative control; and 5, dynamic convergence optimization: collecting a screen mesh pore passing rate, a rice grain group distribution entropy value and system total energy consumption power, constructing a state space, and adjusting vibration frequency, airflow pressure and light intensity parameters by adopting a stability judgment algorithm. According to the method, the screening precision is improved, the energy consumption is reduced, the production efficiency is improved, and the method has remarkable practical application value for the grain processing industry.

The invention discloses an unmanned aerial vehicle hybrid power system configuration optimization method based on a self-adaptive genetic algorithm, and the method comprises the following steps: S1, determining an unmanned aerial vehicle flight platform and collecting task parameters of an unmanned aerial vehicle, the task parameters of the unmanned aerial vehicle comprising flight duration, load capacity W and task environment parameters, recording power consumption of the unmanned aerial vehicle in different flight stages; s2, establishing an unmanned aerial vehicle energy consumption mathematical model, and calculating the total energy consumption of the unmanned aerial vehicle in each flight stage; s3, constructing an optimization objective function of the hybrid power supply of the unmanned aerial vehicle; and S4, performing optimization according to the target function, and outputting an optimal hydrogen fuel cell proportion to enable the target function to be minimum. By optimizing the power distribution proportion of the hydrogen fuel cell and the lithium battery and combining the efficient global optimization capability of the adaptive genetic algorithm, the power supply configuration is dynamically adjusted, and the cruising ability and the energy utilization efficiency of the unmanned aerial vehicle are remarkably improved.

The invention discloses an energy consumption optimization control method and system for heating and ventilation equipment, and the method comprises the steps: carrying out the comprehensive evaluation of the energy consumption condition of the heating and ventilation equipment, and recognizing the current energy consumption condition and energy-saving potential of the equipment; the system is connected with heating and ventilation equipment through the Internet of Things technology, and collects parameters of the heating and ventilation equipment in real time. Based on the preprocessed data, establishing energy consumption models of the water chilling unit, the cooling tower, the freezing pump and the cooling pump; building an energy consumption prediction regression model by adopting a decision tree algorithm, and predicting the total energy consumption of the heating and ventilation system in real time; according to the predicted energy consumption data, an optimization control strategy of the heating and ventilation equipment is formulated in combination with a set energy consumption control objective function; and applying the formulated optimization control strategy to the heating and ventilation equipment, carrying out real-time monitoring and feedback, and carrying out iterative optimization on the control strategy according to an actual effect. According to the method, comprehensive evaluation, real-time monitoring and optimal control of the energy consumption of the heating and ventilation equipment can be realized, and a powerful guarantee is provided for energy conservation and emission reduction of a building.

The invention relates to an air compressor energy-saving control method, device and equipment based on an AI algorithm, and the method comprises the steps: collecting the operation data of an air compressor in real time; the real-time energy efficiency ratio of each air compressor is dynamically calculated according to the energy consumption and the gas production rate, and the air compressors with the energy efficiency ratios lower than 75% are marked as low-efficiency equipment; on the basis of the historical gas consumption data, the production line schedule, the environmental parameters and the equipment state, the gas consumption demand in the future time period and the future efficiency of the air compressor are predicted through the air pressure demand prediction model and the EER prediction model; on the premise of meeting the gas demand in the future period, with the goal of minimizing the total energy consumption and the constraint conditions that the total gas production rate meets the predicted gas demand and the load rate of the frequency converter is limited to be 50%-100%, an optimal switch combination and load distribution scheme is obtained, and a corresponding control instruction is generated and executed, so that the production demand is accurately matched, and meanwhile, the production efficiency is improved. The energy consumption of the system is reduced, the equipment management efficiency is improved, and the purpose of refined and intelligent energy-saving control of the air compression system is achieved.

The invention belongs to the technical field of optimized operation of central air-conditioning systems, and particularly discloses an optimized operation method of an air source heat pump-fan coil central air-conditioning system, which comprises the following steps of: firstly, acquiring influence factors of building load and outdoor meteorological parameters; a central air conditioner load prediction model, an outdoor meteorological parameter prediction model and a data driving model of an air source heat pump and a water pump are constructed, the total energy consumption of system operation, the load demand at the next moment, the outdoor environment temperature and the outdoor relative humidity are obtained through prediction, and then an optimization objective function and constraint conditions are set; and solving the optimization objective function and the constraint condition by using a grid search optimization algorithm to obtain the set temperature of the host at the next moment and the set frequency of the water pump at the next moment, and completing the optimization process. According to the method, the problem of high hysteresis of the central air-conditioning water system is solved, and the host set temperature and the water pump set frequency at the next moment are optimized and solved according to the operation data at the current moment.

The invention relates to the technical field of building energy consumption analysis, and discloses a building energy consumption analysis method and system based on multi-modal perception. According to the method and the system, building environment data, energy consumption data, personnel flow data and equipment state data are collected from a plurality of data sources, the data are cleaned, abnormal values and noise information are eliminated, the data collected at different time points are integrated through a time alignment method, and the data are stored in a database. Data from different sources and dimensions are standardized, feature extraction and modeling are carried out on the preprocessed data, time features, environment features and personnel flow features are extracted, an energy consumption change mathematical model is established, energy consumption analysis is carried out on the extracted features, total energy consumption of a building is calculated for energy efficiency evaluation, and energy efficiency evaluation is carried out. The energy consumption intensity and the energy efficiency ratio are obtained, time sequence analysis is conducted through historical data, the future building energy consumption trend is predicted, the analysis result is visualized, and energy conservation and emission reduction suggestions and optimization decisions are provided based on the visualized analysis result.

This invention advances power grid operational planning by introducing a mid-term scheduling framework for integrated hydro-wind-solar systems that accounts for heterogeneous daily load profiles across multiple receiving-end power grids. The proposed approach utilizes an adaptive variable-step search algorithm to segment loads into peak, flat, and valley intervals. By synthesizing five key metrics, including mean daily load, daily load factor, peak-valley differential ratio, load rates during peak / valley periods, and timing of peak / valley occurrences, the method accurately captures region-specific load patterns and peak-shaving demands. This enables a refined reconstruction of load profiles of receiving-end power grids. A nested multi-temporal scheduling model that couples medium- and short-term horizons to simultaneously maximize total energy production and minimize transmission imbalances among power grids. The model is addressed by using the mixed-integer linear programming (MILP) to obtain medium- and short-term generation schedules and power transmission schedules.

Provided in the embodiments of the present application are an energy storage apparatus, an energy storage system and a charging network. The energy storage apparatus comprises a container, a control module and a thermal management module, wherein the container comprises a container body and a plurality of battery cells, the plurality of battery cells are accommodated in the container body, and at least one of the dimensions of the container in the length direction, the width direction and the height direction is not equal to a corresponding dimension of a standard container; the control module is used for electrically connecting to the plurality of battery cells to perform electrical control over the battery cells; and the thermal management module is connected to the container, the thermal management module is used for managing the temperature of the battery cells, and at least part of the thermal management module is located at the top of the container. When the total energy of a container is fixed, the container can have a smaller volume, such that an energy storage apparatus occupies a smaller floor area, thereby increasing energy per unit floor area of the energy storage apparatus, and thus increasing the areal energy density of the energy storage apparatus.

The invention belongs to the technical field of mobile communication, and relates to an unmanned aerial vehicle adjustment method in a forest fire scene, which comprises the following steps: constructing a system model; the system model comprises an unmanned aerial vehicle model and a forest fire environment model; according to the unmanned aerial vehicle model and the forest fire environment model, constructing an optimization problem by taking minimization of energy consumption of the unmanned aerial vehicle as a target; the optimization problem is converted into a Markov decision process, the optimization problem is solved by adopting a deep reinforcement learning algorithm based on the Markov decision process, and the attitude of the unmanned aerial vehicle is obtained; according to the method, the unmanned aerial vehicle model and the forest fire environment model are constructed by comprehensively considering stroke, smoke and flame spreading in the forest fire scene, the optimization problem is constructed according to the unmanned aerial vehicle model and the forest fire environment model, and the target function of the optimization problem considers the total energy consumption of flight, hovering, data collection and transmission of the unmanned aerial vehicle. Therefore, the method adapts to the actual environment condition of forest fire, and the attitude of the unmanned aerial vehicle can be adjusted more effectively.

The invention relates to the technical field of industrial scheduling, and discloses an excitation-driven demand response-oriented flexible job shop energy-saving scheduling method, which comprises the following steps of: representing an energy-saving scheduling state of a flexible job shop based on an improved heterogeneous disjunction graph, and constructing a graph reinforcement learning model; training the graph reinforcement learning model by adopting a near-end strategy optimization algorithm; and outputting a corresponding scheduling scheme according to the production task and the day-ahead peak clipping invitation based on the trained graph reinforcement learning model. The method effectively coordinates and optimizes the three targets of the completion period, the total energy consumption and the incentive return, provides technical support for power grid operation personnel to design incentive policies, and helps to encourage more industrial loads to participate in power system operation.

The invention belongs to the technical field of liquid cooling system control, and discloses a rotating speed prediction and control method for a charging gun liquid cooling system, and the method comprises the steps: obtaining the performance parameters of key parts, including a radiator, a water pump, a fan, a gun line and a liquid storage tank, in the liquid cooling system; inputting the performance parameters into the one-dimensional simulation model to obtain the highest temperature Tcoolant of the cooling liquid under different working conditions; on the basis of the Tcoolants under different working conditions, the relationship between the different working conditions and the Tcoolants is fitted, so that a rotating speed prediction model is obtained; the environment temperature and the gun line resistor heating power under the target working condition are input into the rotating speed prediction model, under the constraint met by Tcoolant, multiple sets of rotating speed combinations meeting the constraint are obtained through prediction, and the rotating speed combination enabling the total energy consumption of the liquid cooling system to be the lowest is selected from the multiple sets of rotating speed combinations to serve as the optimal rotating speed combination of the target working condition; and during charging, the fan rotating speed and the water pump rotating speed are regulated and controlled in real time based on the optimal rotating speed combination. The system has the advantages of advanced adjustment, quick response, energy conservation, high efficiency and the like.

The invention relates to a clustering and entropy-guided reentrant hybrid flow shop scheduling method. The method comprises the following steps: step 1, establishing a problem model; step 2, setting algorithm operation parameters; 3, adopting an initialization strategy to generate an exploration population and a development population; 4, judging whether a first-stage termination condition is met or not, if not, executing a first-stage evolutionary strategy and an updating strategy on the exploration population and the development population, and otherwise, executing the step 5; 5, constructing an elite population; 6, judging whether a second-stage termination condition is met or not, and if not, executing a second-stage evolutionary strategy on the elite population; otherwise, outputting a Pareto solution set; and 7, updating the elite population. According to the method, dynamic balance of global exploration and local development is realized, and the solution distribution can be improved while the solution set convergence is ensured, so that the completion time and the total energy consumption are reduced, the production cost is reduced, and the workshop scheduling efficiency is improved.

The invention discloses a nondestructive testing method for composite material defects, and relates to the technical field of nondestructive testing, and the method comprises the following steps: firstly, collecting a scanning waveform of a test piece A, determining a potential defect area based on an echo amplitude and a time difference, and outputting a sequence containing space coordinates, an original waveform and focusing parameters; an effective time window is intercepted after preprocessing, sub-bands are generated through wavelet packet decomposition, and total energy is calculated and normalized to obtain an energy vector; secondly, based on a known sample, evaluating the separability of sub-bands by using a Fisher criterion, sorting the sub-bands, determining an optimal energy dimension through cross validation, combining sub-band energy features with phase and time difference features into composite vectors, inputting the composite vectors into a dual-channel lightweight deep network, outputting defect categories and confidence coefficients, and mapping the defect categories and confidence coefficients to a C scanning frame image; and finally, backtracking the three-dimensional coordinates, calculating the defect volume and the residual wall thickness, and comparing with a material performance database to output a conclusion. The problem of misjudgment caused by echo waveform similarity is solved, and detection closed-loop optimization is achieved.

The invention provides an optimization method based on an industrial energy consumption big data model. The optimization method comprises the following steps: S1, acquiring a reference data set; s2, a target function with the purpose of minimizing the total energy consumption within the unit industrial production time is determined, and construction of an energy consumption prediction model embedding layer is completed; s3, enriching data background and influence factors through a real-time energy consumption data set Oj collected from a monitoring station j set in the current industrial production cycle, and realizing real-time calibration and display of the fine-tuned model by establishing a contribution matrix; and S4, forming an energy consumption point set. According to the method, the bag-of-words model is constructed by formulating the constraint conditions, so that the construction and output of the bag-of-words model can meet the constraint requirements of the optimization scheduling model, the obtained model input data is text data with constraint, and the subsequent energy consumption model training and prediction process not only considers the statistical characteristics of energy consumption data, but also considers the energy consumption data. And the computing power constraint in the prior art is reduced.

The invention relates to the technical field of computing power distribution, in particular to a computing network energy integrated collaborative task scheduling platform and scheduling method. The scheduling platform comprises an infrastructure layer, a management and control layer and an application service layer. The infrastructure layer is composed of a computing power supplier, a network supplier and an energy supplier; the application service layer at least comprises a user platform, and the user platform is at least used for receiving and analyzing task requests initiated by all users and reporting the task requests to the management and control layer; the management and control layer at least comprises a sensing module, a measurement module and a computing network energy task scheduling module, the sensing module is used for sensing computing power resources, transmitting network resources and energy resources, and the measurement module is used for measuring the computing power resources, the network resources and the energy resources required for processing each task; and the calculation network energy task scheduling module is used for selecting an optimal calculation power supplier and an optimal network supplier of each task based on the total time delay, the total energy consumption and the total carbon emission. According to the platform, the computing resource utilization rate and the task processing speed can be improved, and carbon emission can be effectively reduced.

The invention discloses an electric vehicle charging power aggregation distributed regulation and control method, and relates to the technical field of AC charging pile charging power regulation and control, and the method specifically comprises the following steps: carrying out the cleaning and stabilization processing of power load and new energy data, employing an ARIMA model to predict the load, and employing an LSTM-Transform model to predict the new energy generation power; the upper layer aggregates the total state of the charging station, including the electricity price, the load, the total vehicle energy, the total docking power of the lower-layer single vehicle model and the battery demand; based on an MCTS enhanced DDPG algorithm, the total charging power is optimized through cost discount rewards and energy penalty; an improved ADMM algorithm distributes total power to each pile, and battery stress and heat loss are minimized; and comparing the actual battery energy with the virtual battery energy, and updating the data and re-deciding when the energy is inconsistent. According to the invention, three industry bottlenecks of poor real-time response of electric vehicle charging, slow large-scale scheduling and difficulty in consideration of economy and battery health are overcome, and power grid dynamic response acceleration, decision-making efficiency jump and comprehensive benefit breakthrough are realized.

The invention discloses a power load distribution method and system based on water-light complementary coordination control, and the method comprises the steps: recognizing control information transmitted by a dispatching center, and obtaining a control mode; and when the control mode is the photovoltaic priority mode, calculating the power generation demand of the hydropower station, and when the control mode is the hydropower priority mode, calculating the power generation demand of the photovoltaic power station. And dynamically adjusting the generating capacity of the photovoltaic power station and the hydropower station to ensure power distribution coordination. According to the power load distribution method and system based on water-light complementary coordination control provided by the invention, power distribution coordination is ensured by dynamically adjusting the generating capacity of the photovoltaic power station and the hydropower station. In the photovoltaic priority mode, the water storage and discharge sequence is optimized through the dynamic planning algorithm, the purposes of minimum total energy consumption and maximum total electric energy are achieved, and the energy utilization efficiency of the system is improved. In the hydroelectric priority mode, the generating capacity is dynamically adjusted through a multi-objective optimization algorithm in combination with real-time load requirements and environmental conditions, and the stability and economic benefits of the system are ensured.

The invention relates to the technical field of mine energy loss cost statistics, in particular to a cost statistics method based on mine energy loss balance, which comprises the following steps: deploying multiple types of sensors in mine equipment and areas to collect data, and carrying out anomaly detection, interpolation supplement and classified storage processing; constructing an energy consumption model by integrating equipment power, load and environmental factors, accounting electric power, fuel and total energy cost according to the energy consumption model, and analyzing the proportion; a genetic algorithm, a particle swarm algorithm and an LSTM model are used to optimize equipment parameters and an energy distribution path, and energy demand and cost are predicted; developing a visual interface to display energy consumption and cost data, generating a decision report, and pushing the decision report to a manager; comparing actual and predicted data regularly, collecting equipment, process and market information, and optimizing an energy loss and cost accounting model and algorithm; according to the method, fine energy management is realized, the utilization efficiency is improved, the cost is reduced, scientific decision support is provided for mine energy management, and the system adaptability and sustainability are enhanced.

The invention relates to the technical field of refrigerating system control, discloses a load distribution control method, system and equipment for a refrigerating host parallel system, and aims to solve the problem of how to find the number of operating refrigerating host nodes corresponding to an optimal energy efficiency value in the parallel system. The method comprises the following steps: acquiring a three-dimensional performance array table corresponding to inlet and outlet temperatures, load rates and energy efficiency values of each refrigeration host; the real-time refrigerating capacity of each refrigerating main machine is obtained, the real-time total refrigerating capacity of the system is determined, and the range of the number of the main machines needing to operate is determined by combining the rated refrigerating capacity and the minimum refrigerating capacity of the main machines; determining the energy efficiency value in the table corresponding to each host under each corresponding number of host combinations in the number range, calculating the total energy efficiency value of each corresponding number of host combinations, and obtaining the number of hosts corresponding to the maximum total energy efficiency value as the optimal number of running hosts; and loading and unloading are carried out based on the current operation number and the optimal operation number. By means of the method, the system meets the refrigeration requirement and meanwhile reaches the optimal energy efficiency value.

The invention discloses a multi-target AGV task allocation optimization method, and the method comprises the steps: obtaining the position information, load information, task information and related constraint conditions of an AGV according to a manufacturing workshop environment and order production demands; performing initialization coding, and generating a task relation matrix, a task data matrix and an AGV state matrix by adopting integer coding for representing a task allocation scheme; constructing a multi-target AGV task allocation optimization model by taking the total transportation distance, the total energy consumption and the load time deviation as optimization targets; solving the multi-objective optimization model by adopting an improved hunting optimization algorithm to obtain an optimal task allocation optimization scheme; according to the method provided by the invention, the problems of load unbalance and the like in AGV task allocation can be effectively solved, the balance and efficiency of task allocation are improved, the task completion time is shortened, the energy consumption of the system is reduced, and the overall operation efficiency of the multi-AGV system is improved.

The present invention relates to the technical field of underground engineering rockburst prevention and control safety. Provided is a quantitative design method for rockburst energy absorption prevention and control. The method comprises: firstly, establishing an underground-engineering finite-element numerical calculation model, and performing underground-engineering excavation simulation calculation; delineating the location and range of rockburst damage and a rockburst pit depth according to an LERR value, and providing the amount of released energy at different positions during a rockburst; then, determining the location, trend and inclination angle of a structural plane; and then, determining the design length of a free section of each energy absorption anchor bolt, and determining an optimal length of each single energy absorption anchor bolt in combination with a design anchoring force of the anchor bolt; then, calculating ejection kinetic energy of rockburst rocks; and finally, determining the length of an anchoring section of each energy absorption anchor bolt, the total length of each anchor bolt, and the number of anchor bolts, such that the total energy absorption capacity of all the energy absorption anchor bolts is greater than the ejection kinetic energy when a rockburst occurs. The method provides a quantitative design method and process for energy absorption anchor bolts, and provides a design basis for scientific design of rockburst energy absorption prevention and control.