1613 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 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 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 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 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 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 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 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 invention relates to the technical field of refrigeration house energy-saving control and digital twinning, in particular to a refrigeration house fan energy-saving operation method and system, and the method achieves the whole-field temperature deduction of a sensor-free area by constructing a CFD reference model and simulating and predicting the three-dimensional transient airflow and temperature distribution in a refrigeration house. Model parameters are calibrated through measured data, and the order of the model is reduced by adopting intrinsic orthogonal decomposition and Galerkin projection, so that the calculation amount is remarkably reduced, and online optimization is supported. And in combination with a data assimilation algorithm, the prediction result of the reduced-order model is continuously corrected, and the adaptability to environment change and system aging is improved. The optimization control stage takes minimization of the total energy consumption of a fan as a target, solves an optimal fan control sequence under the hard constraint that the whole-field temperature does not exceed the cargo safety upper limit and does not exceed the cargo safety lower limit, and adopts a rolling time domain mode for execution to realize collaborative optimization of safety and energy conservation.

The invention provides a multi-stage heat release regulation optimization method and system based on steam extraction energy storage, and relates to the technical field of heat regulation, and the method comprises the steps: building a dynamic thermodynamic model of a heat release process of a steam extraction energy storage system containing a plurality of state variables of heat release power, heat release time, heat release efficiency and temperature change of a heat storage water tank; establishing a multi-objective optimization model by taking minimization of total energy consumption of the system and minimization of entropy production of the heat exchanger as optimization objectives and combining constraint conditions corresponding to the optimization variables; solving the multi-objective optimization model by adopting a hybrid optimization strategy combining an improved non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm, and determining an optimal regulation strategy set of the multi-stage heat release process of the steam extraction energy storage system; the valve position opening degree of the steam extraction electric adjusting valve and the water supply flow of the steam extraction temperature and pressure reduction device are controlled through the steam extraction energy storage control system, optimized adjustment of steam extraction parameters is achieved, meanwhile, the rotating speed of the cold source side water supply and return pump is controlled, and optimized matching of water supply and return parameters is achieved.

A riding lawn mower, including: a frame; a seat; a walking assembly and a walking motor; a cutting assembly and a driving motor configured to drive the cutting assembly; a first energy storage device configured to supply power to at least one of the walking motor or the driving motor, the first energy storage device detachably mounted to the frame and enabled to supply power to a handheld power tool when detached from the riding lawn mower; and a second energy storage device configured to supply power to at least one of the walking motor or the driving motor; wherein a ratio of a total energy of the second energy storage device to a total energy of the first energy storage device is greater than or equal to 2 and less than or equal to 20.

The invention discloses a range extender power generation system and a control method, and relates to the technical field of power systems. The system comprises an energy storage module, a driving module, a range extender module and a control module, the control module combines model predictive control and reinforcement learning and optimizes energy distribution, so that the system can automatically select an optimal strategy according to different driving working conditions, the energy utilization efficiency is further improved, the total energy consumption is reduced, and the driving efficiency is improved. The efficient cooperative work of the internal combustion engine, the generator and the driving motor is realized; self-adaptive gear shifting control is carried out based on real-time vehicle working conditions, path prediction information and motor efficiency, and it is ensured that a driving motor always works in an efficient area. Besides, the system also has fault diagnosis and fault-tolerant control functions, can detect the performance deviation of the engine and the generator in real time, automatically enters a fault-tolerant mode when a fault occurs, and ensures that the vehicle can safely run to a preset place even under the fault condition by cutting off or adjusting the output of the engine.

The invention provides a deep tunnel energy evolution calculation method considering the synergistic effect of surrounding rocks and anchor rods, and belongs to the technical field of tunnel engineering. Comprising the steps that a deep tunnel calculation model considering the synergistic effect of surrounding rock and anchor rods is established, and tunnel model basic parameters, surrounding rock strength parameters and anchor rod material parameters are input; based on the deep tunnel calculation model, solving a stress evolution path of the surrounding rock of the deep tunnel under the synergistic effect of the surrounding rock and the anchor rod after tunnel excavation; solving a stress evolution path of the deep tunnel anchor rod under the synergistic effect of the surrounding rock and the anchor rod after tunnel excavation; calculating the energy density and total energy of the surrounding rock according to the stress evolution path of the surrounding rock; and calculating the energy density and total absorption energy of the anchor rod according to the stress evolution path of the anchor rod. According to the deep tunnel energy evolution calculation method provided by the invention, the problem of solving the strain softening surrounding rock energy field under the elastic-plastic unloading condition is solved.

The invention provides a distributed energy-saving control method and system for refrigeration equipment, and the method comprises the steps: obtaining physical parameters in an environment, fusing the preprocessed data, and generating an environment thermal field model through the mapping of an environment thermal field; according to a time sequence input sequence of the environment thermal field model, obtaining corresponding temperature field time sequence data, performing correction processing on the temperature field time sequence data, generating corresponding future temperature distribution, and forming a predicted future thermal field model; temperature deviation calculation analysis is conducted on future temperature distribution, the cooling capacity demand and the global cooling capacity demand of unit volume air are obtained, and an optimal refrigeration power distribution scheme is designed by minimizing the total energy consumption and meeting the power distribution constraint; and executing the optimal refrigeration power distribution scheme, determining the overall load balance degree of the system according to the current load ratio of each device, performing global optimization scheduling according to the overall load balance degree, and generating a final refrigeration device power distribution scheme.

The invention provides an electromagnetic thermal multi-physical field simulation method based on an FDTD / FVTD algorithm, and belongs to the technical field of electromagnetic simulation. Performing time domain finite difference discretization on the established spatial discrete model by adopting a Yee cellular structure, and calculating electromagnetic field distribution according to a Maxwell rotation equation; constructing an adaptive steady-state judgment criterion based on the instantaneous total energy change rate, and updating electromagnetic field distribution of the spatial discrete model by adopting an FDTD explicit method; calculating the electromagnetic loss power density of the spatial discrete model, and transmitting the electromagnetic loss power density into a thermal field for calculation; based on an FVTD method, thermal field distribution of the discrete model is calculated through an explicit or implicit format, and thermal field distribution information is obtained. The FDTD method is based on explicit time domain iteration, transient behaviors such as electromagnetic wave propagation and scattering can be visually captured, and the FDTD method has broadband response capability and complex medium adaptability and is especially suitable for the high-frequency electromagnetic problem. The FVTD method strictly ensures energy conservation, directly treats boundary heat flux and is suitable for non-uniform materials and complex heat transfer boundary conditions.

The invention provides an arch bridge cable force adjusting method and device, a medium and a program product, and relates to the technical field of arch bridge cable force adjusting.The method comprises the steps that by establishing an arch bridge parameterization finite element model, specific unit force is applied to all suspenders to construct a first influence matrix; singular value decomposition is carried out on the first influence matrix, the first k suspenders with the total energy contribution rate exceeding a first preset value are extracted, and the sensitivity of the suspenders to the global suspender cable force is calculated; calculating an importance index in combination with sensitivity and suspender cable force deviation, and screening the first n important key suspenders to construct a second influence matrix; an adjusted vector is obtained based on the difference value of the second influence matrix and the target influence matrix, and the cable force adjustment amount of the key suspender is solved; the key suspender cable force is sequentially adjusted according to the importance sequence. According to the method, the key suspender is accurately identified through matrix decomposition and sensitivity analysis, the adjustment amount is quantified, interaction is considered, full suspender adjustment is avoided, the construction cost is remarkably reduced, and the cable force adjustment efficiency and accuracy are improved.

The invention relates to the technical field of vehicle range extending control, and discloses a vehicle range extender control method, device and equipment and a storage medium, and the method comprises the steps: obtaining the road section information of each segment of the vehicle in the remaining driving road section; based on the road section information, the required electricity consumption of the vehicle in each section and the total energy recovery electricity quantity corresponding to the remaining driving road sections are obtained; if the sum of the required electricity consumption of the slope section is larger than the total energy recovery electricity quantity, the basic power generation level of at least part of the road sections is increased in the remaining driving road sections to obtain a target power generation level, and a range extender of the vehicle is controlled to generate power based on the target power generation level; wherein the basic power generation level is determined through a preset power generation strategy. Therefore, when the energy recovery electric quantity cannot balance the electricity consumption of the slope section, more electricity is generated in the whole road section by improving the electricity generation level, the whole-course electricity demand is further guaranteed, and the situation that the vehicle generates a large amount of electricity on the uphill road section, and consequently the vehicle NVH performance is poor is avoided.

The invention discloses a power distribution network transformer carbon footprint analysis method based on life cycle evaluation, which is based on a life cycle evaluation method, takes a transformer with the capacity of 200kVA as an example, calculates the carbon emission and the total energy demand of the whole life cycle, and further analyzes the carbon emission in each stage. Research finds that the carbon emission with the highest proportion is the operation stage and accounts for 99.03% of the whole life cycle, the waste recovery stage effectively reduces the influence of the carbon emission on the environment, the technology in the production and manufacturing process is improved, more energy-saving and efficient devices are adopted, and the carbon emission can be effectively reduced. Sensitivity analysis shows that the silicon steel sheets have obvious influence on carbon emission in the life cycle of the power distribution network transformer, and the higher the silicon steel sheets are, the lower the carbon emission is.

The invention discloses an S-wave seismic phase first arrival identification method based on actually measured blasting-induced particle vibration signals, and relates to the field of negative effect monitoring and safety evaluation of blasting vibration induced by rock blasting excavation, and the method comprises the steps: determining a blasting scheme based on a scanning result; arranging a blasting vibration sensor; acquiring actual measurement waveforms of blasting vibration at different blasting center distances; constructing an S-wave seismic phase first arrival recognition function; and obtaining the first arrival moments of the S-wave seismic phases of the actually measured vibration waveforms at different explosion center distances. Compared with the prior art that the recognition precision of the S wave of the blasting vibration signal under the engineering scale is low and the like, by comprehensively utilizing the differences of the S wave, a noise signal, a P wave and a P wave tail wave in the aspects of energy, frequency, polarization characteristics and the like, recognition parameters such as the ratio of transverse energy to total energy, the polarization degree and the deflection angle are introduced, and the transverse energy serves as a weight coefficient; an S-wave recognition function suitable for actual measurement of blasting vibration is provided, and the technology has the advantages of small recognition error, high efficiency and the like.

The invention provides an anti-surge control method. The anti-surge control method comprises the following steps: acquiring historical data of a surge point; inputting the historical data of the surge points into the to-be-trained model to obtain an anti-surge model; based on the anti-surge model, determining an anti-surge line of the fan; the anti-surge line is generated by translating the abscissa of the surge line of the fan in the anti-surge coordinate system rightwards by a first preset unit and translating the ordinate downwards by the first preset unit; the real-time fan inlet flow and the real-time fan total energy head of the fan are obtained, and the real-time working condition point of the fan is determined; whether the real-time working condition point is located below an anti-surge line or not is judged, and if not, the opening degree of an anti-surge valve is adjusted to 100%; if yes, the anti-surge opening degree and the anti-surge valve opening time are determined based on the real-time working condition point, the anti-surge valve is opened according to the anti-surge valve opening time, the anti-surge valve is controlled to operate at the anti-surge opening degree, and the problem that the position area where the working condition point of the draught fan is located cannot be accurately and automatically adjusted at present is solved.