530results about How to "Improve dynamic response performance" patented technology

The invention relates to the technical field of numerical control machine tools, and discloses a five-axis high-precision numerical control machine tool control method, device and equipment and a storage medium, and the method comprises the steps: collecting temperature data and displacement data of a five-axis high-precision numerical control machine tool, and carrying out state space modeling to obtain a machine tool motion state model; stress data and thermal error influence factors of the feed shaft are calculated; feeding shaft stress data and the thermal error influence factors are input into the compensation model, real-time compensation parameters are calculated, compensation control is conducted on the machine tool feeding shaft, and a compensation control result is obtained; according to the compensation control result, an initial machining track optimization model is constructed, and optimized machining parameters are generated; performing segmented machining control based on the optimized machining parameters to obtain real-time machining data; the initial machining track optimization model is optimized based on the real-time machining data, a target machining track optimization model is obtained, and the dynamic response performance and the control precision of the five-axis high-precision numerical control machine tool are improved through the method.

The invention discloses a central air conditioner energy-saving optimization system based on load prediction, and relates to the technical field of air conditioner energy saving.According to the central air conditioner energy-saving optimization system based on load prediction, a VMDTransformerGRU hybrid deep learning model is introduced, through multi-source data fusion, the precision of cold load prediction is remarkably improved, and the prediction model can recognize the load change trend in advance. According to the improvement, the dynamic response capacity of the system is effectively enhanced, pre-regulation and control can be carried out before load changes, energy waste is reduced, a water chilling unit, a cooling tower and a pump are modeled to be independent intelligent agents, multi-agent deep reinforcement learning (MADRL) is adopted, and all the intelligent agents can autonomously learn and optimize control strategies of the intelligent agents. The intelligent agents cooperate with one another, efficient operation of the whole system is achieved through autonomous decision, and operation conflicts between devices are avoided. In this way, dynamic coordination and joint optimization among all the devices are achieved, invalid energy consumption is reduced, and the overall energy-saving effect and operation stability of the system are remarkably improved.

The invention relates to the technical field of electrical system evaluation, in particular to a toughness evaluation method of a novel power system in extreme weather, which comprises the steps of constructing a digital twin model, collecting data in real time, performing fusion processing on multi-source data, identifying potential risks of the power system in extreme weather, determining system vulnerability and constructing an extreme weather scene. The method comprises the following steps: simulating weather events possibly occurring in the future, predicting the occurrence probability and influence range of extreme weather, analyzing system vulnerability, identifying key nodes and vulnerable equipment, defining and calculating a toughness index, optimizing an operation strategy, executing an emergency plan when the extreme weather occurs, evaluating a plan execution effect, and continuously optimizing the plan through feedback; according to the method, the dynamic response capability and the toughness evaluation accuracy of the power system in extreme weather are remarkably improved, the actual operation state of the power system is reflected in real time, the model parameters can be dynamically adjusted through the digital twin technology, and the real response of the system under the changing condition is simulated.

The invention relates to the technical field of flexible screens, and discloses a flexible screen brightness control method and device based on region segmentation, and the method comprises the steps: carrying out the multi-view image collection of a plurality of bending regions of a flexible display screen, obtaining target image data, and calculating the deformation compensation partition data of the flexible display screen; gridding brightness sampling is carried out on the deformation compensation partition data to generate brightness uniformity characteristic data, LED driving parameter calculation is carried out based on the brightness uniformity characteristic data, and a driving compensation matrix is output; inputting the driving compensation matrix into a second-order state observer for real-time current estimation and temperature feedback, and generating a partition driving control signal; and performing multi-region collaborative optimization on the partition driving control signal to realize brightness uniformity control of the flexible display screen in different bending states. According to the method, the influence of temperature drift on the display uniformity is eliminated, the stability of the driving current is improved, and the display deviation caused by temperature change is remarkably reduced.

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 multi-modal digital twinborn scene building method and device based on image semantic fusion, and relates to the technical field of data processing and digital twinborn scenes. The method comprises the steps of obtaining an image data set and a text data set of a building construction site, and performing preprocessing; inputting the preprocessed image data set and text data set into a natural language processing model to obtain semantic description auxiliary data; inputting the preprocessed image data set into a target detection algorithm to obtain target extraction image data containing a target detection frame; fusing the output results of the two targets to obtain a fusion box of each target; then, a perspective projection algorithm is adopted to calculate a coordinate position corresponding to each target in the BIM, and three-dimensional world coordinates are obtained; and inputting the three-dimensional world coordinates into the BIM by adopting Dynamo to realize the building and real-time updating of the multi-modal digital twinborn scene. According to the method, the accuracy and robustness of scene recognition are remarkably improved, and the defects of a traditional method in the aspect of multi-modal data processing are overcome.

The invention discloses a smart factory management platform and management method based on digital twinning, and particularly relates to the technical field of digital twinning, and the method comprises the following steps: carrying out the division and preliminary analysis of a digital twinning smart factory, carrying out the feature extraction and anomaly analysis of components with hidden dangers, and carrying out the feature extraction and anomaly analysis of the components with hidden dangers; calculating a data source quality index and a prediction performance quality index, and evaluating the comprehensive operation quality by using a pre-trained machine learning model; based on the evaluation result, dividing the components into low-efficiency and high-efficiency operation components; judging an optimization mode for the low-efficiency component; performing intelligent regulation and control on the efficient component based on the initial sampling frequency; the method can comprehensively evaluate the operation state of the digital twin model assembly, evaluate the adaptation degree of each assembly and the actual factory assembly, intelligently regulate and control the high-efficiency operation assembly, optimize the low-efficiency operation assembly, significantly improve the resource utilization rate, ensure the stability and reliability of the system under various operation conditions, and improve the reliability of the system. And system faults and shutdown are prevented.

The invention relates to the technical field of lithium battery temperature regulation and control, in particular to a temperature regulation method and system based on an embedded lithium battery. According to the invention, accurate real-time monitoring and dynamic regulation and control of the temperature of the lithium battery are realized by constructing the temperature regulation and control sensing model and the self-adaptive power management system. And a temperature propagation model, a dynamic battery model and a compensation factor are introduced, so that the temperature prediction precision and the system response capability are improved. A comprehensive thermal state evaluation function and an innovative temperature adjusting method are adopted, and comprehensive thermal state evaluation and flexible temperature control are achieved. And the multi-level temperature risk management ensures the safety of the system. The multi-layer and multi-angle temperature control strategy obviously prolongs the service life, safety and performance of the battery, and provides a reliable and efficient energy solution for equipment such as electric automobiles.

The invention relates to the technical field of motor control, and discloses a hybrid excitation synchronous motor control strategy optimization method and device. The method comprises the following steps: performing mathematical modeling on permanent magnet flux linkage, exciting current and rotor parameters of the hybrid excitation synchronous motor to obtain a state observer equation; collecting current parameters and position parameters, and performing filtering processing to obtain motor operation state parameters; inputting a five-layer deep neural network for current and rotating speed parameter prediction to obtain an optimal current parameter and an optimal rotating speed prediction parameter; constructing a multi-objective function, and solving to obtain a current reference value; inputting the current reference value into a cascade PI controller, and performing voltage regulation through feedforward decoupling control and a space vector pulse width modulation technology to obtain a control voltage signal; and performing performance calculation control parameter optimization on the control voltage signal to obtain a target control parameter, thereby improving the control precision of the hybrid excitation synchronous motor.

The invention discloses a real-time monitoring and early warning method for the leakage state of a long-line type public facility, and relates to the technical field of monitoring and early warning of the leakage state of the long-line type public facility, and the method specifically comprises the following steps: analyzing environment dynamic response information stored in a central monitoring system; respectively generating a soil humidity change coefficient, an environment pressure response index and a temperature influence compensation coefficient; constructing a leakage risk assessment model for the generated soil humidity change coefficient, the environment pressure response index and the temperature influence compensation coefficient, and generating a leakage risk coefficient; and determining a preset leakage risk coefficient threshold value, comparing the determined leakage risk coefficient threshold value with the generated leakage risk coefficient, evaluating whether a leakage risk exists in the pipe gallery area close to the earth surface, and respectively generating a normal signal and an abnormal signal according to an evaluation result. According to the invention, the problem of underground pipe gallery leakage monitoring is solved, abnormal signals are accurately distinguished, and the monitoring accuracy and safety are improved.

The invention relates to the technical field of subsynchronous oscillation suppression, in particular to a system for suppressing subsynchronous oscillation of a doubly-fed grid-connected system, and the system comprises a doubly-fed generator module which is used for converting wind power into electric energy; the subsynchronous oscillation spectrum disturbance module specifically comprises a current disturbance generation unit and a phase decoupling optimization unit; and the multi-frequency domain decomposition and absorption module specifically comprises a frequency spectrum decomposition unit and a self-adaptive absorption unit. According to the invention, the current disturbance generation unit actively injects a low-power and non-linearly distributed current disturbance signal, dynamically destroys the coherence of the subsynchronous oscillation signal, and combines the spectrum decomposition unit to accurately identify the subsynchronous oscillation dominant frequency and the harmonic component thereof, thereby refining the distribution analysis of the oscillation energy, and improving the accuracy of the subsynchronous oscillation. The high-precision target positioning capability is provided for the oscillation suppression strategy, the multi-level oscillation suppression from active disturbance to precise control is realized through the cooperative operation of the two, and the stability and adaptability of the system under the complex power grid condition are effectively improved.

The invention relates to the technical field of battery management, in particular to a new energy vehicle battery data analysis method and system, and the method comprises the following steps: merging a state feature set of a battery based on the charging and discharging period and use environment data of the new energy vehicle battery, extracting the voltage, current and temperature features of the state, and obtaining a state feature set; and carrying out feature combination on the battery degradation state, classifying and associating feature items and temperature features in the feature combination, and obtaining a state feature merging result. According to the method, through merging the new energy vehicle battery charging and discharging period and the use environment data, the battery state characteristics are accurately extracted, refined classification and correlation analysis are realized, the discrimination capability and adaptability of the state data are improved, and the state transition trend is flexibly captured based on the linkage trend of the state transition frequency and the current observation data; and dynamic evaluation is realized through correlation of periodic temperature and current, an accurate and reliable state change evaluation scheme is provided, and the dynamic response of battery management and the full-life periodic performance regulation and control quality are optimized.

The invention discloses a method for collecting orchard ecological environment information, and the method comprises the steps: collecting soil parameters, space environment parameters, insect pest parameters and fruit parameters of an orchard in real time through deploying a plurality of collection devices, and carrying out the collection to form an orchard ecological basic data set; a backstepping model is constructed to indirectly calculate key ecological parameters including soil nutrients, fruit quality and insect pest diffusion trend, and an optimization result is corrected through experimental verification and the model; comparing a backstepping result with an orchard target value to analyze a deviation source, and establishing a macroscopic cause chain through data matching and a visual tool; according to the deviation analysis and the cause chain result, determining an executable regulation and control measure matched with the regulation and control direction; and monitoring the effect after the implementation of the regulation and control measures, verifying the regulation and control effect by comparing the data before and after the implementation of the regulation and control measures, and applying feedback data to iterative optimization of the backstepping model. The method is scientific and efficient and can be used for improving orchard production benefits and ecological environment quality.

The invention relates to the technical field of high-speed motor intelligent control, and discloses a high-speed motor intelligent control method and device for a cooling fan, and the method comprises the steps: carrying out the parameter collection of a high-speed fan motor, and obtaining a motor basic parameter group; performing motor overmodulation boundary calculation to obtain a linear PWM region threshold value and an overmodulation region threshold value; performing multi-objective optimization operation on the voltage distortion degree, the current harmonic quantity and the system loss to obtain an initial control parameter group; performing current harmonic analysis to obtain a current harmonic compensation value and a system delay compensation value; performing online state evaluation operation to obtain a PWM control parameter correction value, a current controller parameter correction value and a voltage vector distribution correction value; and performing fault feature analysis on the running state of the high-speed fan motor, and outputting a fault phase isolation instruction and a fault-tolerant control instruction, thereby improving the overall running effect of the high-speed fan motor.

The invention discloses a capacitive small six-dimensional force sensor, an optimization design method and a six-dimensional force decoupling method.The sensor comprises a three-T-shaped beam type elastic body, a lower electrode plate, an upper electrode plate and a base, the three-T-shaped beam type elastic body converts external six-dimensional force into displacement changes, the upper electrode plate is connected with the elastic body, the upper electrode plate and the lower electrode plate form six sets of opposite electrodes, and the three-T-shaped beam type elastic body is connected with the base. Comprising three groups of horizontally arranged electrodes and three groups of vertically arranged electrodes to form six groups of parallel plate capacitor structures. When the sensor is subjected to external force, deformation of the elastic body drives the upper electrode plate to generate displacement, a capacitance value is changed, and force information is converted into six capacitance signals to be output. The sensor is compact in design and small in size, and can be conveniently integrated into narrow spaces such as robot fingertips. Based on a capacitance measurement principle, the sensor has excellent dynamic response performance and can quickly respond to external force change. The sensor has high sensitivity, rigidity in all directions is consistent, the coupling effect of force is effectively reduced, and high-precision six-dimensional force decoupling is achieved by combining a decoupling algorithm.

The invention provides a vegetation prediction model construction method, a vegetation prediction method, a vegetation prediction device and vegetation prediction equipment, and relates to the technical field of data processing. The vegetation prediction model construction method comprises the following steps: taking ecological environment data of a target area as an independent variable, taking vegetation spatial distribution data corresponding to the target area as a dependent variable, and training a first random forest model to obtain a vegetation spatial distribution prediction model; taking the output of the vegetation parameter spatial distribution prediction model and the period-by-period meteorological data as independent variables, taking the period-by-period vegetation feature change data as dependent variables, training a second random forest model, and obtaining a vegetation feature time change prediction model; and splicing the vegetation spatial distribution prediction model and the vegetation feature time change prediction model to obtain a vegetation prediction model. According to the technical scheme, the data utilization efficiency can be improved, the model training time is shortened, and meanwhile the adaptability of the vegetation prediction model and the accuracy of vegetation prediction are improved.

The invention relates to the technical field of micro switch production line intelligent scheduling methods, in particular to a micro switch production line intelligent scheduling method and system based on a graph neural network, and the method comprises the steps: obtaining an attribute set of nodes, and constructing a node graph of a production line; the dynamic representation module is used for dynamically representing real-time states of nodes; generating a multi-scale state prediction model based on the aggregation weight matrix, and obtaining a state prediction matrix for predicting the state change trend of the equipment; based on the state prediction matrix, constructing a nonlinear global optimization scheduling strategy, and further optimizing task allocation and resource allocation in the production line to obtain a global optimal scheduling scheme; constructing a layered feedback correction mechanism based on the global optimal scheduling scheme; based on a hierarchical feedback correction mechanism, an intelligent maintenance trigger signal is generated for identifying potential faults and triggering preventive maintenance, deep modeling and state prediction are performed on each node of the production line, and efficient task scheduling and equipment maintenance management are realized.

The invention belongs to the technical field of numerical control, and particularly discloses a following error control method and system for a swing shaft of a numerical control machine tool, and the method comprises the steps: in the movement process of the machine tool, a numerical control system calculates the speed feed-forward quantity, the acceleration feed-forward quantity and the swing shaft gravitational torque feed-forward quantity at the current moment according to an instruction signal of a swing shaft i in a current control period k; sending the speed signal to a driver to obtain a feedback speed signal and a signal output by a current loop; and the LESO in the driver estimates the external disturbance quantity of the current control period based on the feedback speed signal and the signal output by the current loop, and feeds forward and compensates the external disturbance quantity into the current loop to complete the feed motion in the current control period. The following position error of the swing shaft of the numerical control machine tool is reduced, the following precision control level matched with a linear shaft is achieved, and then the final machining precision is improved.

The invention relates to the technical field of sensors, and discloses a full-speed-domain position-sensorless control method for a permanent magnet synchronous motor. The full-speed-domain position-sensorless control method for the permanent magnet synchronous motor comprises the following steps: S1, realizing a high-frequency pulsating injection method in a zero-low-speed domain, injecting a high-frequency pulsating signal into a motor system, and extracting rotor position information according to system response characteristics; s2, estimating the position and the speed of a rotor in real time in a medium-high speed domain by adopting a super-spiral sliding mode observer method; and S3, adopting a composite control strategy, and combining the two methods to realize full-speed-domain composite control. According to the full-speed-domain position-sensorless control method for the permanent magnet synchronous motor, the high-frequency pulsating injection method and the super-spiral sliding-mode observer method are combined, position-sensorless high-precision control over the permanent magnet synchronous motor in the full-speed domain is achieved, the control performance is effectively improved, the system cost and complexity are reduced, and the control precision is improved. And the application prospects in multiple fields are widened.

The invention provides a high-power high-frequency modular battery cluster charging and discharging test device, which is characterized by comprising an alternating current coordination controller connected with a middle computer and used for receiving an instruction transmitted by the middle computer and distributing current to a plurality of isolated ACDC modules; the input ends of the plurality of isolated ACDC modules are connected in parallel and then are connected to the AC co-controller and a three-phase mains supply, and the output ends of the plurality of isolated ACDC modules are connected in parallel and then form a DC bus for converting AC into DC and realizing voltage isolation; and a plurality of BUCK-BOOST modules, the input ends of the plurality of BUCK-BOOST modules are connected in parallel and then are connected to the DC bus, and the output ends of the plurality of BUCK-BOOST modules are connected in parallel and then are connected to a target battery cluster for testing the charging and discharging of the target battery cluster. Modular design is achieved, efficient charging and discharging capacity is achieved, the defects that a traditional scheme is large in size, high in cost, slow in dynamic response and the like are overcome, equipment efficiency and flexibility are improved, and the device is suitable for a high-power scene.

The invention discloses a low-power-consumption fire behavior real-time auxiliary first aid method and system based on edge calculation, relates to the technical field of fire behavior auxiliary first aid, and is suitable for crowded areas of public buildings such as schools, hospitals, high-rise buildings, hotels and shopping malls. Environmental information such as personnel density, temperature and smoke concentration is collected in real time, and fire detection and dynamic evacuation path planning are carried out by using an edge calculation unit. The intelligent terminal adjusts the brightness and the color based on the dynamic path, and sound reminding is assisted, so that clear audio-visual guidance is realized. Under the condition of fire outage, through the optimized lighting strategy, the lighting strategy of each area is dynamically adjusted, so that the energy consumption is reduced, the service time of a standby power supply is effectively prolonged, and the safety and efficiency of personnel evacuation are effectively guaranteed.

The invention discloses a power module seamless switching control method and system, and the method comprises the steps: obtaining the current output voltage of a power module in a low-voltage power distribution network, and carrying out the voltage distortion monitoring of the power module; if the power module generates voltage distortion and the distortion degree exceeds a preset threshold value, determining that the power module is a module to be switched; obtaining a current load parameter, and determining a switched target module from the standby power modules according to the load parameter; based on a preset control parameter library, control parameters of the target module are determined, and the target module is pre-adjusted; switching the to-be-switched module into an offline state, and switching the target module into a running state; and obtaining the current output voltage of the target module, performing transient response evaluation on the target module, and correcting the control parameters of the target module according to an evaluation result. According to the invention, intelligent seamless switching of the power module when voltage distortion occurs can be realized, and the power supply reliability of the low-voltage power distribution network is improved.

The invention discloses a permanent magnet synchronous motor high-performance prediction control method based on a sliding-mode observer, and the method comprises the steps: S1, collecting the position and mechanical angular velocity information of a rotor, collecting the three-phase voltage and current data of a permanent magnet synchronous motor, and converting the three-phase voltage and current data into the voltage and current of a motor stator under a two-phase rotating coordinate system; s2, constructing a speed ring sliding-mode observer, estimating electrical and mechanical parameter perturbation interference and load torque interference on line, designing a rotating speed ring deadbeat prediction controller, outputting reference current at the next moment, and setting the amplitude limit of the output reference current; s3, a current loop sliding mode observer is constructed, electrical parameter perturbation interference is estimated online, a current loop prediction controller is designed in combination with the dead-beat prediction control principle, and voltage at the next moment is output; and S4, carrying out amplitude limiting modulation on the output voltage, obtaining a voltage under a static coordinate system through Park inverse transformation according to the voltage after amplitude limiting modulation, and outputting a switching signal to a three-phase inverter by combining a two-level space vector modulation module to drive the permanent magnet synchronous motor to operate.

The invention discloses an iterative phase-locked limit cycle flux observer, a control method, a control device and a control system, and the control method comprises the following steps: collecting alpha-beta axis voltage and current signals of a motor, constructing an IPMSM mathematical model under an alpha-beta axis, and calculating to obtain a counter electromotive force vector of the motor; taking the motor back electromotive force vector as input, acquiring motor rotor flux linkage information through an iterative phase-locked limit loop flux linkage observer, and estimating a motor rotating speed and a position signal according to the motor rotor flux linkage information; and the rotating speed and the position signal of the motor are input into a motor closed-loop control system, so that position-sensorless closed-loop control of the permanent magnet synchronous motor is realized. According to the method provided by the invention, the problem of integral drift or phase lag of a traditional flux linkage observer can be avoided, the influence of direct current bias on flux linkage and position observation precision is effectively suppressed, the dynamic response capability of position sensorless control is remarkably improved, and a position estimation error generated in a dynamic process is suppressed.

The invention discloses a time sequence power grid planning method based on a new energy consumption rate, and particularly relates to the technical field of power grid planning. By collecting and analyzing new energy output fluctuation and load change data in different time periods in a power grid, a new energy and load fluctuation curve is constructed, the harmonic distortion severity degree is evaluated in combination with real-time data of harmonic monitoring equipment, the load influence and reactive power margin fluctuation of harmonic waves on a reactive power regulation device are identified, and the reactive power regulation device is optimized. The power quality risk is quantified based on the comprehensive analysis result, the time period is divided into high risk, medium risk and low risk levels, a power quality improvement scheme is formulated, and in the medium risk time period, the output quantity of a reactive power regulation device and the starting frequency of a harmonic filter are dynamically adjusted by predicting the voltage support risk, so that the power quality is improved. And the voltage stability of the power grid under the harmonic fluctuation condition is ensured, so that the power quality and the safety and stability of new energy consumption are remarkably improved.

The invention relates to the technical field of robot control, in particular to a robot rescue emergency control system based on voice interaction. The system is used for performing control voice signal acquisition and rescue emergency voice control task structured analysis by using a built-in voice receiving function of a robot, and generating rescue emergency voice control task structured data; acquiring panoramic image data, environment sensing data and long-distance laser radar data monitored by the robot; the method comprises the following steps: performing global image point cloud model construction and multi-level environment change collaborative optimization processing based on panoramic image data, environment perception data and long-distance laser radar data monitored by a robot, and generating an optimized global image perception model; and performing rescue emergency intelligent control operation on the robot based on the rescue emergency voice control task structured data and the optimized global image perception model. According to the invention, the voice instruction is received through the voice interaction function, so that automatic and efficient robot rescue emergency control is realized.

The invention discloses a VSG control method based on parameter self-adaption and hybrid energy storage SOC adjustment. From the perspective of improving power grid frequency fluctuation under new energy grid connection, important factors such as an action mechanism of a virtual rotor angular frequency variation and a change rate, influence of rotational inertia and a damping coefficient on VSG power frequency response output characteristics, SOC balance adjustment of hybrid energy storage and the like are comprehensively considered; according to the method, the adaptive control of VSG parameters and the coordinated control of the hybrid energy storage SOC are realized, so that the purposes of reducing the response time and overshoot of VSG power frequency output, reducing the decrease amplitude of the SOC of the energy storage system and reducing the frequency fluctuation of a power grid in a transient process are achieved. Compared with a traditional VSG control method, the dynamic response capability of the VSG can be effectively enhanced, the service life of an energy storage system is prolonged, the transient stability of a power grid is improved, and the method has important guiding significance and engineering application value for design and optimization of a VSG controller.

The invention belongs to the technical field of power electronics, and relates to a hybrid device common bridge arm three-level split output inverter modulation method and system, and the method comprises the steps: obtaining a mapping phase angle, a modulation degree and a region number according to a reference voltage vector generated by a three-phase original modulation wave; mapping the phase angle, the modulation degree and the switching period according to the size of the modulation degree to obtain two kinds of vector action time; generating two modulation waves according to the area number and the action time of the two vectors; generating a first pulse driving signal according to the area number, the first modulation wave and the forward and reverse carrier waves; generating a final carrier according to the area number and the forward and reverse carriers, and generating a second pulse driving signal according to the second modulated wave, the forward carrier, the forward carrier of the previous period and the final carrier; and selecting a driving signal from the two pulse driving signals according to the area number. The modulation degree can be maximized, driving pulses are respectively generated in different modulation degree ranges, the output current quality is high, and the total harmonic distortion rate is low.

The invention discloses a dynamic traffic distribution and signal control combined optimization method, and belongs to the field of intelligent traffic. The method specifically comprises the steps that firstly, a road section impedance function is constructed, node impedance is introduced to obtain an intersection impedance combination function, and the intersection impedance combination function is optimized through the traffic flow, density and speed relation based on dynamic adjustment parameters, vehicle type differences, weather conditions and traffic flow speed to obtain a combination optimization traffic impedance model; and solving the combinatorial optimization traffic impedance model by using a particle swarm algorithm to output a traffic distribution scheme. According to the method, dynamic traffic distribution and adaptive signal control are integrated, the road network emergency response capability is improved, traffic prediction and signal regulation and control synchronization is realized, and an accurate impedance model is introduced to dynamically adapt to driver preferences, so that congestion is relieved, and travel time and resource allocation are optimized.

The invention discloses a power supply system containing a hybrid energy storage and diesel generator two-stage emergency power supply, which relates to the technical field of power supply systems and comprises an alternating current bus, a system power supply, an energy storage device, a diesel generator, an important load and an unimportant load. The system power supply comprises a frequently-used power supply and a standby power supply which are respectively connected to the AC bus through switches; the energy storage device consists of a hybrid energy storage system, a three-phase inverter, a filtering unit and an energy storage switch, and is connected to the alternating current bus; the diesel generator is connected to the bus through the grid-connected switch; the loads are divided into important loads and unimportant loads, and the system has four switchable states of common power supply-energy storage parallel connection, standby power supply-energy storage parallel connection, energy storage power supply and diesel generator-energy storage parallel connection so as to ensure stable power supply.