1201 results about "Current load" patented technology

A superconducting distributed bidirectional current driver system includes multiple bidirectional current drivers, a bidirectional current load being operatively coupled between two adjacent bidirectional current drivers. Each of the bidirectional current drivers includes first and second superconducting latch circuits. The first superconducting latch circuit in a first one of the bidirectional current drivers and the second superconducting latch circuit in a second one of the bidirectional current drivers coupled to the current load are selectively activated by first and second activation signals, respectively, to establish a first current path through the current load in a first direction. The second superconducting latch circuit in the second one of the bidirectional current drivers and the first superconducting latch circuit in the first one of the bidirectional current drivers are selectively activated to establish a second current path through the current load in a second direction opposite the first direction.

The invention relates to the technical field of power batteries, in particular to a battery aging state detection system, method and device. The method comprises the following steps: obtaining battery pack structure design data of the unmanned aerial vehicle; according to the battery pack structure design data, carrying out single battery health monitoring, and generating a multi-parameter feature vector; establishing a data mapping relation of the battery monomer indexes through the multi-parameter feature vectors, thereby obtaining initial health state data of the battery pack; collecting charging and discharging sample data of the unmanned aerial vehicle under different flight conditions; a dynamic current excitation sequence is constructed based on charging and discharging sample data, and current load changes in different flight stages are simulated, so that a multi-working-condition current disturbance sequence is generated. By simulating the load change of the unmanned aerial vehicle in different flight stages and combining the dynamic response capture capability of the high-frequency current sensor, the electrochemical characteristics of the battery can be extracted in real time, and the aging state of the battery under the complex working condition can be accurately identified.

The invention relates to a switching power supply control method, system and device and a storage medium, and the method comprises the steps: monitoring the voltage and current of the output end of a switching power supply, and the rate and amplitude of load change, and obtaining the real-time data of the load change; according to the real-time data of the load change, the change of the current load is identified, and a load change mode is obtained; dynamically adjusting operation parameters of the controller according to the load change mode; based on the real-time data of the load change, predicting the load change trend of the switching power supply in real time by using a pre-trained load prediction model to obtain a prediction result, and generating a disturbance compensation signal based on the prediction result; and adjusting the switching state of a switching circuit of the switching power supply according to the control parameter adjusted by the controller and the disturbance compensation signal. The switching power supply has the effect of improving the stability of the switching power supply.

The invention provides a frequency converter dynamic fault diagnosis method and system based on a multi-brand load test platform. According to the method, multiple real-time operation parameter sets are acquired, the amplitude attenuation threshold and the frequency fluctuation interval of a voltage waveform fragment are dynamically adjusted according to a load mode configuration file, the dynamic voltage reference parameter corresponding to the current load mode is generated, and then the actual waveform and an expected current phase curve are compared frame by frame; the method comprises the following steps: firstly, calculating to obtain a phase offset, then constructing a multi-dimensional operation characteristic spectrum, then carrying out time sequence cross validation on a sudden change time node of a power factor jump trajectory and an abnormal waveform position in a harmonic distortion incidence matrix, outputting an abnormal mode code, and finally, matching a load adaptation grade constraint condition to generate a diagnosis report; according to the technical scheme provided by the invention, the fault diagnosis precision and the cross-brand system adaptation capability in a complex load scene are remarkably improved, and the problem of misjudgment of a single brand threshold rule in a multi-device scene is effectively solved.

The invention relates to the technical field of intelligent control, in particular to an intelligent power optimization scheduling method in a new energy grid-connected scene, which comprises the following steps of: acquiring a current load, a rated load, an adjustment rate and a temperature rise value through a data interface, and normalizing to generate a characteristic parameter matrix; the product of the adjusting rate and the temperature rise value is used for calculating the correlation degree with the aging factor to obtain an elastic weight, extracting the voltage stability and the weight, inputting the voltage stability and the weight into a Dijkstra algorithm to screen a path meeting the safety margin, monitoring the slope of an inertia change curve, adjusting a compensation coefficient through fuzzy PID control, and feeding back the compensation coefficient to correlation degree calculation. According to the method, multi-source parameters are fused through a rule tree structure, the load regulation rate and the aging factor weight are dynamically distributed, bus voltage stability and elastic weight path optimization are combined, a synchronous inertia margin safety correction system is constructed, the system response precision, the state cooperation efficiency and robustness are improved, local optimum is avoided, and the system reliability is improved. And the standby capacity configuration and the frequency advance correction capability are optimized.

The invention discloses a cold station multi-equipment combination energy efficiency optimization method and system, relates to the field of cold station energy efficiency optimization, and aims to solve the problems of low energy efficiency and response lag caused by manual adjustment. The method comprises the following steps: S1, acquiring key operation data (power, state, temperature, flow and frequency) of a water chilling unit, a cooling tower and a water pump and external environment data (temperature and humidity, wind speed and load prediction) in real time; s2, constructing an equipment energy efficiency model based on historical data, and quantifying the change of the equipment efficiency along with the load rate, the temperature difference and the environment temperature; s3, according to the current load and environment prediction, simulating and traversing the energy consumption of the equipment start-stop combination and the water pump frequency combination by applying a Monte Carlo algorithm, and screening a combination strategy with the lowest total energy consumption of the system; s4, converting the optimization strategy into a control instruction, and issuing the control instruction to equipment for execution; and S5, comparing the actual energy consumption with the predicted value, and if the deviation exceeds 5%, correcting model parameters by adopting a least square regression algorithm to realize closed-loop optimization. And the energy efficiency and the response speed of the cold station are obviously improved.

The invention provides a power grid cable temperature monitoring and alarming method, device and equipment and a storage medium. The method comprises the following steps: acquiring cable outer sheath temperature data through a temperature sensor and acquiring cable current load data and health condition indexes; calculating the temperature of the conductor according to the temperature data of the outer sheath, the current load data and the environmental parameter data by using a superposition principle calculation model based on time attenuation coefficient correction; according to the cable health condition index, calculating a grading early warning threshold value, and comparing the conductor temperature with the threshold value to determine an early warning grade; and when the early warning level reaches a preset condition, matching the sheath temperature data with a template in an abnormal mode library, identifying an abnormal reason through multiple algorithms, and generating a disposal suggestion in combination with geographic information system data. According to the invention, the conductor temperature can be quickly and accurately calculated, the early warning threshold value is dynamically adjusted, the abnormal reason is intelligently identified, and the accuracy and effectiveness of cable temperature monitoring and early warning are greatly improved.

The invention relates to the technical field of power supply current waveform adjustment. The power supply current waveform self-adaptive adjustment method comprises the steps of obtaining real-time operation information of a power supply, analyzing the real-time operation information through a load trend recognition algorithm, judging whether the load state of the real-time operation information changes or not, obtaining a judgment result, and determining the current waveform of the power supply according to the judgment result. And if the judgment result is that the load state is changed, predicting a target current output waveform matched with the current load state by using the trained current prediction model based on the real-time operation information, and performing real-time adjustment on the output current of the power supply according to the target current output waveform to obtain a power supply output result. According to the invention, the self-adaptive adjustment effect of the current waveform is realized.

The invention discloses a wind power tower lifting platform based on self-adaptive weight and fuzzy PID control and an adjusting method of the wind power tower lifting platform. The lifting platform comprises a main frame body, a ladder stand component, a guide rail assembly, a lifting platform mechanism and a lifting driving assembly. The device comprises a transmission chain wheel assembly, a driven chain wheel assembly, a counterweight assembly, an anti-falling assembly, a top corridor, a sliding terminal and a conductive sliding frame. The adjusting method comprises the steps of sensor installation and data acquisition; processing and analyzing data; self-adaptive weight optimization adjustment is carried out, the load condition and the environment condition of the current platform are judged, and the optimal balancing weight position is calculated in real time; ensuring that the lifting speed is matched with the current load according to a weight optimization result; and carrying out multi-layer neural network load prediction. The stability and safety of the platform under complex working conditions are improved, and the problems that a traditional method is slow in response and insufficient in adjusting precision when dealing with load fluctuation and environment change are solved.

The invention relates to the technical field of variable-frequency centrifugal fans, and discloses an energy-saving optimization control method and system for a variable-frequency centrifugal fan, and the method comprises the steps: collecting a real-time parameter data set of the variable-frequency centrifugal fan through a sensor network, and carrying out the digital filtering and feature extraction, and obtaining a target feature vector; inputting the target feature vector into a trained VentNet dual-network deep neural network for prediction calculation to obtain an optimal rotating speed prediction value and an energy consumption prediction value under the current load condition; calculating control error data and inputting the control error data into a hyperbolic law of arrival controller for processing to obtain a frequency adjustment instruction of the frequency converter; the frequency adjusting instruction is sent to the frequency converter, the actual rotating speed of the variable-frequency centrifugal fan is adjusted, and the variable-frequency centrifugal fan operates in the optimal energy-saving state, real-time energy-saving optimization control over the variable-frequency centrifugal fan under different load conditions is achieved, the energy consumption amplitude is reduced, and meanwhile the stability and reliability of operation of the fan are guaranteed.

The invention discloses a secondary frequency modulation optimization control method under a low-load working condition, relates to the technical field of power grid resource optimization, and is used for solving the problem that a control strategy under the low-load working condition is not fully considered in the prior art. The current load interval is recognized, and control parameter correction is conducted on the unit located in the low load interval; calculating a total output power reference value; carrying out calculation by adopting a self-adaptive optimization algorithm, and carrying out power fine distribution to obtain an optimal charging and discharging instruction of each energy storage unit; and the output power is adjusted in real time through double-closed-loop control. The invention further discloses a secondary frequency modulation optimization control device under the low-load working condition, electronic equipment and a computer storage medium. By dynamically correcting the control parameters of the low-load thermal power generating unit and cooperatively distributing the hybrid energy storage power in combination with the adaptive optimization algorithm, the safety and economical efficiency of the energy storage system are guaranteed.

The invention provides an optical storage parallel network control method, which comprises the following steps: calculating the current load rate percentage of each residual unit according to the connection relation and electrical path information of the residual units after isolation; evaluating the available margin capacity and the frequency regulation bearing capacity of the remaining units, matching the available margin capacity and the frequency regulation bearing capacity with a specific power value and a frequency regulation requirement which need to be compensated, and determining a feasible unit combination scheme for fault recovery; generating a power redistribution list and a frequency adjustment responsibility distribution table according to the direct current bus connection mode of the optimal unit recombination, and obtaining an output power set value and a frequency adjustment parameter of each unit after adjustment; and extracting the continuous power supply time value of the optical storage parallel network, updating the total power supply capacity value of the optical storage parallel network, and maintaining the continuous power output and frequency stability after the fault of the optical storage parallel network.

The invention discloses a self-adaptive algorithm optimization control system and method for intelligent energy management, and relates to the technical field of energy management, and the method comprises the steps: determining the load change sensitivity according to a load change prediction rate; grouping the devices according to the load change sensitivity and the current load state to obtain a plurality of device load groups; mapping the load change prediction rate and the multi-dimensional dynamic threshold matrix to obtain a target control strategy; determining an initial control parameter according to the target control strategy; performing difference analysis on the operation parameter and the initial control parameter to obtain a control parameter difference value; adjusting the target control strategy according to the control parameter difference value so as to update the initial control parameter; the updated initial control parameters are input into an execution control unit; the adjusted load state of the building equipment is fed back in real time; according to the invention, accurate real-time prediction and control of building equipment load change are realized, and the stability and response efficiency of intelligent building energy management are effectively improved.

The invention discloses a gas-steam combined cycle unit power plant load prediction and optimal scheduling method and system based on artificial intelligence, and relates to the technical field of artificial intelligence. Load data and unit output of a gas-steam combined cycle unit power plant are recorded in real time; load fluctuation characteristics are extracted based on the load data, prediction error feedback is obtained, an adaptive time window mechanism is introduced, and the time window length of next prediction is adjusted according to the current load fluctuation characteristics and the prediction error feedback; extracting a multi-time scale fusion feature of the load data of each time step in the time window according to the updated time window length; constructing a load prediction model, and predicting future load data based on the multi-time scale fusion features of each time step; based on the current unit output, the future load data and the self-adaptive time window mechanism, constructing a reinforcement learning model to optimize the unit output adjustment value of the gas-steam combined cycle unit power plant; and the self-adaptability of load prediction and optimal scheduling is realized.

The invention discloses a hybrid power energy-saving control method suitable for mining equipment, and belongs to the technical field of mining equipment energy-saving control, and the method comprises the following steps: collecting real-time operation state data, including load power, gradient, speed and battery SOC, of the mining equipment through multiple sensors; based on the collected real-time operation data and historical data, predicting a load demand in a future time window by using a time sequence prediction model; according to the current load prediction result, the power distribution proportion of the internal combustion engine and the motor is dynamically adjusted, so that the hybrid power system meets the operation requirement; and in the deceleration or idling stage of the equipment, the braking energy is recovered through reverse working of the motor, and the recovered energy is stored in the battery or the super capacitor. Through dynamic load prediction, energy distribution optimization and flexible energy recovery, efficient energy saving of mining equipment and reasonable utilization of energy of the internal combustion engine and the motor under complex working conditions are achieved.

The invention relates to a stepping motor control method and device based on load torque, which mainly performs closed-loop control on a stepping motor by collecting current and voltage signals so as to optimize the working efficiency of the motor. According to the method, firstly, a current signal is filtered and compared with a target reference value, a current deviation is calculated, and if the deviation exceeds a threshold value, a target PWM signal is generated to adjust the current until the deviation is reduced to be within a preset range. When the current meets the requirement, a voltage commutation point and a current zero point are determined based on the voltage signal, the current load state and torque value are calculated by combining the time interval between the voltage commutation point and the current zero point with preset no-load time and out-of-step time, and the working current is adjusted according to the information, so that the working efficiency of the stepping motor is optimized.

The invention discloses a factory equipment predictive maintenance scheme optimization method based on reinforcement learning, and relates to the technical field of equipment maintenance scheme prediction, and the method comprises the steps: carrying out the fusion through employing a space-time attention mechanism based on an equipment health index, equipment operation duration and a current load level, generating an equipment state vector, constructing a reinforcement learning framework, and carrying out the optimization of a factory equipment predictive maintenance scheme. A multi-target reward function is embedded in the reinforcement learning framework, a PPO algorithm is adopted for optimization, a reinforcement learning model is generated, the reinforcement learning model is trained by using the equipment state vector, the trained reinforcement learning model is obtained, the trained reinforcement learning model is deployed to an actual industrial control unit, the latest equipment state vector is received in real time, and the final equipment state vector is obtained. And obtaining an optimal maintenance strategy. And converting the optimal maintenance strategy into a specific maintenance instruction and executing the specific maintenance instruction. Through the factory equipment predictive maintenance method, rapid response to emergencies is facilitated, greater loss is avoided, resource allocation is optimized, and enterprise competitiveness is enhanced.

The invention discloses a power consumption control method and device for a power distribution network monitoring control chip and a storage medium, and the method comprises the steps: carrying out the clock domain division of the power distribution network monitoring control chip, and obtaining a target clock domain; obtaining current load data and historical load data of the target clock domain; according to the historical load data, trend prediction processing is carried out by using an autoregressive moving average model to obtain predicted load data; according to the predicted load data and the power consumption state of the control chip, performing action evaluation processing by using preset reinforcement learning to obtain a state action function value; calculating a to-be-adjusted clock frequency and a to-be-adjusted voltage according to the current load data; determining a predicted clock frequency and a predicted voltage according to the state action function value; and performing power consumption control on the target clock domain according to the target clock frequency and the target voltage to obtain a power consumption control result. The power consumption control is realized, and the resource utilization rate and the control efficiency are improved. The method can be widely applied to the technical field of electric power engineering.

The invention provides a charging pile power distribution method and system based on dynamic load balancing, and the method comprises the following steps: obtaining the local state information of each charging pile, the state information at least comprises the charging queuing length, the current vehicle charge state, the current load power, the battery health state, the predicted residence time, the electricity price information and the power grid load state; based on the historical charging behavior, the electricity price fluctuation and the traffic flow data, the vehicle access frequency, the electricity price trend and the power demand of each charging pile in the future time period are predicted through a time sequence neural network; a prediction model is constructed; according to the method, forward-looking power planning is realized by entering a multi-dimensional prediction mechanism; the power distribution efficiency is improved by adopting a local collaborative game; the self-adaption and generalization ability is realized through reinforcement learning, and the robustness of a scheduling strategy in multiple scenes is enhanced; through a multi-target scheduling index, the electricity price, the battery life, the waiting time and the power grid load are optimized at the same time.

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 relates to the technical field of energy management and control, in particular to a micro-grid energy management and control system based on energy storage and distributed photovoltaic, and the system comprises a load prediction scheduling module which collects real-time grid data, analyzes the current load and photovoltaic generating capacity, carries out the time sequence analysis, and generates real-time load prediction data. According to the invention, in the load prediction, the real-time load prediction data generated based on time sequence analysis is combined with the photovoltaic power generation fluctuation, the charging and discharging strategy of the energy storage equipment is dynamically corrected, and the energy supply mismatching caused by the load fluctuation is reduced. According to the causal inference method, a multi-layer causal chain is constructed, key influence factors in photovoltaic operation are identified in combination with real-time load prediction data, real-time adjustment of energy distribution is realized through feedforward correction of fault early warning signals, and equipment overload or energy waste caused by imbalance of energy flow directions is avoided.

A method of operating a microgrid system includes: obtaining a microgrid dispatch schedule input that includes a schedule of required electrical energy generation for the microgrid system for a given time period; filtering the scheduled dispatch input and converting the scheduled dispatch input into power levels to meet power demands for required electrical energy generation of the dispatch schedule input for the microgrid system; receiving a current load level for the microgrid system based on one or more electrical loads electrically coupled to the microgrid system; comparing the schedule of required electrical energy generation to an actual required electrical energy generation for the microgrid system; and dispatching one or more electrical assets in real time to meet a difference between the scheduled required electrical energy generation and the actual required electrical energy generation.

The invention discloses a power load prediction method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring feature data related to a power load variable in a historical time period; based on weather forecast data in the feature data, a target scene type corresponding to the current load prediction scene is identified, and the target scene type is at least a daily load prediction scene type or a cold-wave weather load prediction scene type; a target power load prediction model corresponding to the target scene type is called to predict the feature data to obtain a prediction result, and the target power load prediction model at least comprises a daily power load prediction model corresponding to the daily load prediction scene type and a daily power load prediction model corresponding to the daily load prediction scene type. Or a cold-wave weather power load prediction model corresponding to the cold-wave weather load prediction scene type, wherein a prediction result is used for at least representing a power load value in a future period of time. The technical problem of low accuracy of power load prediction is solved.

The invention provides a new energy vehicle charging pile safety performance life prediction method. The method comprises the steps of collecting real-time operation monitoring equipment data of a new energy vehicle charging pile; constructing a key component degradation model to evaluate the life degradation trend of the current charging pile; adopting a time decay model based on the residual life of the current charging pile; constructing a group life prediction model, and predicting the overall residual life of the charging pile group in the micro-region according to the failure risk probability of the current charging pile; evaluating a current load optimization strategy by using the micro-region environment data in combination with the dynamic load characteristics; and designing a load optimization strategy to maximize the overall life of the charging pile group in the micro-region, and generating an active maintenance plan based on the priority maintenance list. According to the invention, full-process closed-loop management from data acquisition to load modeling, life prediction and active optimization is realized, and an efficient and accurate solution is provided for safety performance and life management of the charging pile.

The invention discloses a power adjustment method and device, a storage medium and a program product, and belongs to the technical field of computers. The method comprises the steps of obtaining first state information of an energy storage system when it is detected that first power is smaller than preset power, wherein the first power is current load power of a grid-connected point of a microgrid; and obtaining one or more pieces of second state information of the energy storage system when the first state information is in discharging or charging. And adjusting the operation power of the energy storage system according to the first state information, the one or more pieces of second state information and preset power so as to realize the adjustment of the load power of the grid-connected point. According to the invention, when a reverse power risk exists, the probability of a reverse power phenomenon can be reduced by dynamically adjusting the load power of the grid-connected point. The anti-reflux device does not need to be installed and maintained, so that the cost can be saved. Moreover, the operation power of the energy storage system is adjusted preferentially, so that the influence on the power generation system can be avoided as much as possible, and the electric energy utilization rate of the power generation system is ensured.

A bidirectional on-board charger includes a direct current bus, two bus capacitors, a power factor correction circuit, and a controller. The power factor correction circuit includes: a first bridge arm; and three second bridge arms, separately connected in series between the positive electrode and the negative electrode of the direct current bus. The controller is configured to: control the first switch (K1) to be turned off, and control at least one of the second bridge arms to output a direct current to charge a power battery; or control the first switch (K1) to be turned on, and control a midpoint of at least one of the second bridge arms to output an alternating current to supply power to an alternating current load. A Vehicle power system and an electric vehicle are further disclosed. The charger reduces circuit costs.

The invention provides an intelligent optimization control method for the energy consumption of a packaging production line, and the method comprises the steps: collecting the processing data of the production of products of two specifications of vacuum can sealing and normal pressure can sealing, extracting the load change of a conveying belt during the switching of different specifications, and obtaining the fluctuation range of the load of the conveying belt; predicting the operation power scheduling of the equipment according to the instantaneous peak value and high-frequency fluctuation characteristics of the power demand, and outputting energy consumption peak value prediction data of the equipment during specification switching; determining and outputting an operation time sequence plan according to the energy consumption peak prediction data of the equipment during specification switching; monitoring the dynamic change of the conveyor belt load in real time according to the operation time sequence plan, obtaining the deviation value of the current load and the predicted load, and judging whether the deviation value affects the energy consumption distribution mode of the whole production line or not; and through the new energy consumption distribution result, generating and executing an equipment cooperative operation control instruction in combination with the operation time sequence plan.

The invention provides a combined fleet power distribution method and system based on intelligent control, and relates to the technical field of data processing, and the method comprises the steps: predicting the total power demand of a fleet at a future moment according to effective operation data and historical operation data; calculating the power output capability data of each ship based on the maximum power output limit, the current load condition and the operation efficiency of the ship by combining the operation state of each ship; based on the power margin, the power efficiency and the task priority of each ship, the power demand is distributed among the ships; a corresponding power instruction is sent to a power system of each ship, and power output of each ship is adjusted; monitoring the actual operation state of the fleet, obtaining operation feedback data, comparing the operation feedback data with the power demand data, and if the deviation exceeds a preset deviation threshold value, updating the effective operation data; the autonomy and accuracy of power distribution of the combined fleet are improved.

The invention discloses a regulation and control method and device for a load power supply loop, equipment, a medium and a program product, and relates to the technical field of servers, and the method comprises the steps: obtaining the current change rate, the contact temperature rise rate and the voltage change rate of a load interface when a hot plug event occurs, so as to comprehensively reflect the rapid change condition of a circuit state at the hot plug moment; then, inputting the data into a surge probability prediction model to obtain a surge occurrence probability so as to pre-judge a surge risk in advance; and when the surge occurrence probability is greater than or equal to a first preset probability, the resistance-capacitance parameters of the load power supply loop are calculated according to the monitored load interface current, load interface voltage and load interface temperature, so that when a hot plug event occurs, the circuit can be rapidly adjusted according to the resistance-capacitance parameters, abnormal changes of the current and the voltage are inhibited in time, and the reliability of the hot plug event is improved. The protection response speed is improved, equipment damage caused by untimely protection is effectively avoided, and the server is ensured to stably operate under complex conditions such as load hot plug.

The invention relates to the field of large language models, in particular to a resource allocation method and system based on tensor parallelism, and provides a resource allocation method in order to solve the problems of how to meet calculation requirements under the condition of insufficient calculation resources and how to prolong the service life of equipment. Comprising the steps that calculation groups are divided, and the optimal calculation load and the maximum calculation load of the calculation groups are calculated; dividing the sub-modules into tensor modules or assembly line modules, and dividing priorities; calculating load capacity; calculating communication efficiency; calculating the load margin; allocating the sub-modules to each computing group to obtain a computing resource allocation plan; if the current load is greater than or equal to the maximum calculation load, preferentially calculating a part of sub-modules, and marking the sub-modules which are not preferentially calculated as to-be-distributed modules; and distributing the to-be-distributed module to the calculation group for assisted calculation.