333 results about "Hybrid drive" patented technology

The invention discloses a permanent magnet synchronous motor temperature monitoring method based on digital-analog hybrid drive, and the method specifically comprises the steps: dividing a motor into a plurality of heat source nodes, including a stator winding, a stator tooth part, a rotor core and the like, connecting the nodes through a thermal resistance and thermal capacity network, building a thermal network model of the motor, and carrying out the thermal network model; calculating the initial values of thermal resistance and thermal capacity of each component of the motor, and optimizing parameters in a thermal network model by using a differential evolution algorithm based on the lumped parameter thermal network model and in combination with a data-driven strategy. Furthermore, temperature rise prediction is dynamically adjusted according to motor state data (such as stator current, rotating speed and the like) collected in real time so as to realize real-time temperature estimation. The method is high in temperature estimation precision, does not affect the performance of the motor, is simple in calculation, is good in real-time performance, and has physical interpretability. The temperature rise of the motor can be predicted in real time under different working conditions, early warning is given out, motor faults caused by too high temperature rise are effectively avoided, and the reliability and safety of the motor are improved.

The invention discloses an electric power inspection exoskeleton modular structure, and belongs to the technical field of human body exoskeleton machine assistance, the electric power inspection exoskeleton modular structure comprises a supporting system, the supporting system comprises a back supporting plate, a waist supporting frame and thigh supporting rods, and a main body supporting frame is formed; the modular interface system comprises at least one group of mechanical interfaces, electrical interfaces and data interfaces which are uniformly distributed on a force transmission path of the support system; the functional module comprises a plurality of interchangeable power assisting modules, operation modules and protection modules, and the functional module is detachably connected with the supporting system through the modular interface system; a hybrid drive system includes a rigid driver and a flexible driver for providing position control and compliance force control, respectively. By means of the modular interface system, plug-and-play of functional modules such as power assisting, operation and protection is achieved, needed exoskeleton configuration can be rapidly assembled, multiple sets of exoskeleton equipment do not need to be arranged, and the equipment utilization rate and task adaptability are greatly improved.

A hybrid drive omni-directional motion actuator based on an origami structure, comprising at least one actuating module. The actuating module comprises a columnar shell deforming based on the origami structure, an upper cover plate and a lower cover plate. The columnar shell is the origami structure derived from Waterbomb crease, and a cavity containing gas is formed inside the shell. The two ends of the cavity are open, and the openings are respectively connected with the upper cover plate and the lower cover plate for blocking the openings. The lower cover plate is provided with vent holes communicating with the cavity, and the upper cover plate and the lower cover plate are pulled by driving ropes arranged in a crossed manner. Through the cooperation of gas and tendon drive, the columnar shell can have deformations of contraction, bending and torsion, so that a single actuating module has omni-directional movement capability.

The invention provides a hybrid drive system and a vehicle with the same. The hybrid drive system comprises a differential mechanism, a first half shaft of the differential mechanism is connected with a left front wheel, and a second half shaft of the differential mechanism is connected with a right front wheel; the driving motor is provided with a motor input shaft, the motor input shaft is connected with the first half shaft through a first gear pair, the output torque of the driving motor is transmitted to the first half shaft through the motor input shaft and the first gear pair, and then the left front wheel is driven to rotate; the generator is provided with a generator input shaft, the generator input shaft is connected with the second half shaft through a second gear pair, and the output torque of the generator is transmitted to the second half shaft through the generator input shaft and the second gear pair so as to drive the right front wheel to rotate. According to the scheme, the left front wheel and the right front wheel can be independently driven, independent torque control is provided for each wheel, a distributed driving mode under electric driving is achieved, and the technical problem that a hybrid driving system in the prior art cannot adapt to complex road surfaces is solved.

The invention relates to the technical field of robots, in particular to a hybrid drive leg device and a humanoid robot. The leg device comprises a thigh assembly and a shank assembly. The thigh assembly comprises a first driving mechanism, a second driving mechanism, a thread shifting device and a pair of first structural plates. The first driving mechanism comprises a first motor, a first conical wheel, a first guide wheel and a first rope. The first motor drives the first conical wheel to rotate and drives the first structural plate to swing. The shank assembly comprises a pair of second structural plates, and the second driving mechanism is used for driving the second structural plates to swing. The thread shifting device drives the first rope to move in the axial direction of the first conical wheel and drives the second rope to move in the axial direction of the second conical wheel, so that the leg device can stably operate under the conditions of different gaits and loads. In addition, the leg device of the scheme comprises the elastic buffer piece and the hydraulic damper, the elastic buffer piece and the hydraulic damper can absorb impact force borne by the leg device in real time in the movement or landing impact process of the leg device, and the overall stability of the leg device is improved.

Hybrid drive snowmobile comprising an IC engine, a variable speed drive comprising a first pulley connected to a drive shaft of the IC engine, a second pulley and a belt coupled to the first and second pulleys, an intermediate shaft-connected to the second pulley of the variable speed drive, an output shaft connected to the intermediate shaft by a transmission and a drive wheel of a motor track of the snowmobile connected to the output shaft and a reversible electrical machine connected to the intermediate shaft.

This invention provides a hybrid power drive device and a vehicle having the same. The hybrid power drive device includes an engine, a first motor, a second motor, and a compound planetary gear set. The output end of the engine is driven to the motor shaft of the first motor. The motor shaft of the second motor drives the wheels to rotate. A first sun gear meshes with a first planetary gear, and both the second sun gear and the first planetary gear mesh with a second planetary gear. The second planetary gear meshes with a ring gear, and the ring gear is driven to the wheels. A brake controls the operating state of the second sun gear, a first clutch controls the engagement or disengagement of the engine output end with the first sun gear, and a second clutch controls the engagement or disengagement of the engine output end with the planetary carrier. The technical solution provided in this application solves the problem in related technologies where hybrid power drive devices struggle to simultaneously achieve efficiency and compactness across multiple operating modes, as well as flexible drive switching.

The invention discloses a learning and model hybrid driven autonomous vehicle safety control method and system, which are suitable for expressway convergence control in a hybrid traffic environment. According to the method, multi-agent reinforcement learning and a safety potential field theory are combined to construct a hybrid control framework; collaborative decision and adaptive control of ramp and main line automatic driving vehicles are realized through a multi-agent near-end strategy optimization algorithm; meanwhile, a novel relatively safe potential field energy index is introduced, the interactive collision risk between the vehicles is quantified in real time, and a reward function is designed based on the index to guide an intelligent agent to learn safer behaviors; in addition, a dynamic safety intervention mechanism is further arranged, when risk assessment exceeds a preset threshold value, protective action coverage is automatically triggered, and risk avoidance is implemented. According to the method, the collision rate is remarkably reduced, meanwhile, high passing efficiency and reward revenue are kept, and the safety, coordination and deployability of the automatic driving vehicle are effectively improved.

The present invention relates to a hybrid drive system (1) of a motor vehicle. The hybrid drive system (1) comprises an internal combustion engine (10) and a transmission (20), wherein the internal combustion engine (10) has an output shaft (11) and the transmission (20) has an input shaft (21), wherein the hybrid drive system (1) comprises an axial flux machine (30), wherein the axial flux machine (30) has a stator (34) and a rotor (31) rotatable relative to the stator with at least one rotor disk, wherein the hybrid drive system (1) comprises a dual-mass flywheel (70), wherein the output shaft (11) of the internal combustion engine (10) is rotationally fixedly coupled to a first flywheel of the dual-mass flywheel (70), wherein the rotor (31) of the axial flux machine (30) is rotationally fixedly coupled to a second flywheel of the dual-mass flywheel (70), and wherein the input shaft (21) of the transmission (20) is connected to the rotor (31) of the axial flux machine (30). is coupled in a rotationally fixed manner.

The present invention relates to a hybrid drive system (1) of a motor vehicle. The hybrid drive system (1) comprises an internal combustion engine (10) and a transmission (20), wherein the internal combustion engine (10) has an output shaft (11) and the transmission (20) has an input shaft (21), wherein the hybrid drive system (1) comprises an axial flux machine (30), wherein the axial flux machine (30) comprises a stator (34) and a rotor (31) rotatable relative to the stator with at least one rotor disk (32), wherein the axial flux machine (30) is arranged between an engine block (16) of the internal combustion engine (10) and a transmission housing (26) of the transmission (20).

The invention relates to the technical field of computer vision and artificial intelligence, and discloses a public area pedestrian trajectory monitoring system based on cross-camera tracking and hybrid simulation completion. The system comprises a camera array module, a pedestrian detection and cross-camera tracking module, a monitoring blind area information base module, a hybrid drive pedestrian simulation engine module, a track fusion and complementation module and a simulation integration propulsion module. According to the invention, monitoring blind area tracks can be effectively filled, track continuity and prediction accuracy are improved, existing hardware is fully utilized to reduce cost, and modular design is easy to integrate and convenient to use. The generated complete track can be widely applied to scenes such as public safety monitoring, passenger flow analysis and emergency evacuation plan making in public places such as ports, subway hubs, cruise ship wharfs and commercial complexes, and supports a real-time monitoring or post-analysis mode.

The invention discloses a dual-fuel ship energy efficiency operation index prediction method based on physical data hybrid drive. The method comprises the steps that historical energy efficiency data of a liquefied natural gas dual-fuel ship are collected and preprocessed; and obtaining input features through feature selection. Constructing a physical mechanism model to predict fuel consumption, and establishing a two-way recurrent neural network data driving model based on a Bayesian optimization and whale optimization two-stage optimization algorithm framework to predict an error between the physical mechanism model and each piece of historical ship fuel consumption data; and adding each fuel consumption predicted based on the physical mechanism model and an error output by the data driving model through a physical data hybrid driving model formed by combining the data driving model and the physical mechanism model to obtain a final prediction result of each fuel consumption, and calculating to obtain a ship energy efficiency operation index. According to the method, the relative error of the energy efficiency operation indexes of the liquefied natural gas dual-fuel ship obtained through prediction can be smaller, and the actual energy efficiency management requirement is met.

A hybrid power drive system (100), comprising: an engine (10), a driving shaft (20), first electric motors (40), second electric motors (60), a planetary gear mechanism (30), and a limiting unit (50). The second electric motors are transmittingly connected to the driving shaft; the planetary gear mechanism comprises a first rotating member (31), a second rotating member (32), and a third rotating member (33) that can rotate coaxially; the first rotating member is connected to an output end of the engine, the second rotating member is transmittingly connected to the driving shaft, and the third rotating member is connected to the first electric motors; and the limiting unit is configured to limit the transmission of torque from the first electric motors to the engine and to form a torque transmission path between the first electric motors and the driving shaft. The system can ensure the power performance and economy of vehicles. In addition, the present invention further relates to a vehicle (200).

The invention discloses a flexible self-adaptive grabbing finger based on a paper folding structure and a method, and relates to the technical field of manipulators. The finger comprises a finger seat and at least two finger bodies, wherein each finger body comprises a joint module, a finger section module and a connecting piece; the joint module is a first hollow body formed by a triangular variant Yoshimura paper folding structure in a surrounding mode, an air bag is arranged on the back side of the joint module, and a first guide hole is formed in the ventral side of the joint module. The finger section module is a second hollow body; the connecting piece is detachably connected with the joint module and the finger section module; the first rope penetrates through the first guide hole to be connected with the ventral side of the finger section module at the tail end, and the second rope penetrates through the second guide hole in the connecting piece to be connected with the middle position of the finger section module at the tail end. A paper folding structure and air-rope hybrid driving are adopted, and through the hierarchical control strategy of pneumatic rapid enveloping and rope-driven event driving, the structure is simplified, control is reliable, maintenance is convenient and fast, the self-adaptive grabbing capacity is achieved, and the mechanical arm is particularly suitable for agricultural picking and other complex environments.

The invention provides a multi-energy-flow real-time updating method based on a building integrated energy system, and belongs to the technical field of energy scheduling, and the method comprises the steps: obtaining an equipment set and a typical state vector, and constructing a building integrated energy mechanism model based on mechanism data and balance constraint data; collecting a plurality of historical operation data, and determining a plurality of period-mode historical data and a plurality of environment clustering data; determining a plurality of response clustering data, and constructing a data driving model of each environment label of each period-mode label; acquiring equipment parameter data, and determining fitting residual data of each environment label of each period-mode label; performing first optimization on each data driving model; and constructing a mechanism-data hybrid drive model and realizing adaptive optimization. The method can accurately adapt to a subdivided scene, improve deviation fitting precision, give consideration to explanatory and prediction precision, dynamically adapt to system time-varying characteristics, improve the adaptability of the model in a complex scene, and guarantee long-term prediction reliability.

The invention discloses a seawater intrusion dynamic early warning method and platform, and relates to the technical field of machine learning, and the method comprises the steps: collecting monitoring data through a monitoring well sensor network to form a multi-source time series data set; constructing a hybrid drive prediction model containing a physical model branch, a data drive branch and a fusion layer; pre-processing the multi-source time sequence data set, and inputting the pre-processed multi-source time sequence data set into the hybrid drive prediction model to obtain a seawater intrusion prediction value; and inputting the seawater intrusion prediction value into a pre-trained intrusion risk classifier, evaluating different degrees of seawater intrusion probabilities in a preset time window, and triggering an early warning signal of a corresponding level. According to the invention, the technical problems that the water body source is difficult to distinguish and the seawater intrusion trend determination is inaccurate in the traditional seawater intrusion early warning are solved, and the technical effects of accurately distinguishing the seawater intrusion water body source, dynamically predicting the intrusion trend, triggering the corresponding grade of early warning, and effectively improving the seawater intrusion early warning accuracy and the control pertinence are achieved.

The invention belongs to the technical field of vehicle tests, particularly relates to a damping durability detection device for a plug-in hybrid drive vehicle, and aims to solve the problems that a detection device can only simulate vertical jolt, is single in test working condition and is seriously inconsistent with complex multi-dimensional loads in actual driving, results are distorted, dampers need to be disassembled and assembled repeatedly during batch detection, and the test efficiency is low. In order to solve the problem of low efficiency, the invention provides the following scheme: a test tool cabinet is provided. The device for detecting the damping durability of the plug-in hybrid drive vehicle has the advantages that a combined load under a real road condition can be simulated through the multi-dimensional impact module, vertical impact can be performed, and intermittent impact in deflection, inclination and horizontal directions can also be realized; according to the utility model, complex working conditions of a vehicle during bumping, turning or lateral stress are comprehensively restored, and the module supports one-key rapid clamping and dismounting, is suitable for batch continuous detection of different types of shock absorbers, and remarkably improves the test efficiency and the result reliability.

The invention discloses a data-physical hybrid driven hydrogen-induced fatigue life prediction method, which comprises the following steps: S1, selecting different hydrogen storage steel lining materials, respectively carrying out a tensile test and a low-cycle fatigue test before and after hydrogen charging, collecting mechanical property parameter data, fatigue parameter data and fatigue life, and calculating the fatigue life of the hydrogen storage steel lining materials according to the mechanical property parameter data, the fatigue parameter data and the fatigue life; carrying out normalization processing on the collected data; s2, constructing a cascade PINN model composed of a BP model and a PINN model; s3, the non-hydrogen-filled mechanical property parameters serve as input, the hydrogen-filled mechanical property parameters serve as output, and a BP model is constructed; the hydrogen charging mechanical property parameters output by the BP model and the fatigue parameters serve as input of a PINN model together, the fatigue life under the hydrogen charging working condition serves as output, and the PINN model is constructed; and taking a prediction result finally output by the PINN model as the fatigue life under the hydrogen charging working condition. According to the method, the fatigue life of the hydrogen storage steel lining material stored for a long time can be rapidly predicted.

The application discloses a kind of hybrid drive's CTS wind tunnel test motion system's missile model workspace calculation method, comprising the following steps: (1) establish the coordinate system of hybrid drive's CTS wind tunnel test motion system;(2) establish the kinematics model of hybrid drive's CTS wind tunnel test motion system, establish the mapping relationship of each drive quantity in hybrid drive's CTS wind tunnel test motion system and the pose of missile model;(3) establish the constraint condition of hybrid drive's CTS wind tunnel test motion system;(4) pose assignment rule is formulated, and the working space of missile model is synthesized using the method of attitude traversal, pose superposition.It can be determined that the working space of missile model in wind tunnel through the above steps, the calculation efficiency of the motion space of missile model in CTS test is greatly improved, and the basis for the determination of CTS test trajectory boundary is laid.

The present invention relates to a cadence control method for a hybrid drive applicable to electric bicycles, the hybrid drive comprising a muscle power input (PM) and an electric motor (eM1), both being connected to a power-split gear (PS), the power output (LS2) of which is mechanically connected to a wheel (RM). The hybrid drive is equipped with a control device (HMI) that allows the user to select a preset cadence (Wc(prog)) and said control method acts on the motor (eM1), thereby increasing or decreasing its power to slow down or accelerate the pedalling cadence (Wc), keeping its value close to the preset cadence (Wc(prog)). This method applies to both pedal-driven vehicles with power-split hybridisation with a single electric machine, and vehicles equipped with an e-CVT system with parallel hybridisation at the output, in which the cyclist also selects a level of assistance.

The invention provides a hybrid power driving system and a vehicle, the system comprises a first driving mechanism and a second driving mechanism, the first driving mechanism comprises an engine, a first clutch, a second clutch, a first motor, a first gear transmission mechanism and a first differential mechanism; the second driving mechanism comprises a second motor, a second gear transmission mechanism and a second differential mechanism; the engine is used for outputting power; the first motor is used for converting power output by the engine into electric energy; the first clutch is used for connecting or disconnecting an engine and the first motor; the second clutch is used for connecting or disconnecting the first motor and the first driving mechanism; the first gear transmission mechanism is arranged between the second clutch and the first differential mechanism and is in transmission connection with the second clutch and the first differential mechanism; the second gear transmission mechanism is arranged between the second motor and the first differential mechanism and is in transmission connection with the second motor and the second differential mechanism.

The invention discloses a model and data hybrid driven PEM fuel cell life prediction method. The method comprises the following steps: acquiring endurance test data; based on the endurance test data, respectively combining an equivalent circuit model and a polarization curve model to carry out aging characteristic analysis on the PEM fuel cell to obtain aging characteristics; processing the aging characteristics, and extracting a mixed aging index in combination with a part aging model; the mixed aging indexes are input into a life prediction model after being normalized, the aging trend of the PEM fuel cell is predicted, the remaining service life is estimated, and the life prediction model is constructed through optimization of a Bi-LSTM neural network. The aging trend of the PEM fuel cell can be predicted, and the remaining service life can be accurately estimated.

A hybrid drive system (200) for a work machine (100) includes a drive system (202). The drive system (202) includes an engine (204) that generates a first energy supply. The drive system (202) also includes a generator (206) mechanically coupled with the engine (204) to drive the generator (206). The drive system (202) further includes a battery system (208) that generates a second energy supply. The drive system (202) includes a motor generator unit (210) mechanically coupled with the generator (206) to drive the generator (206). The motor generator unit (210) is driven by the battery system (208). The hybrid drive system (200) also includes a controller (216) communicably coupled with the drive system (202). The controller (216) is configured to control the drive system (202) to direct, towards one or more traction motors (110, 112) of the work machine (100), a portion of at least one of the first energy supply via the generator (206) and the second energy supply via the motor generator unit (210) and the generator (206).

This invention discloses a hybrid drive system based on a three-speed transmission, including a front axle power assembly, a three-speed hybrid transmission, a rear axle drive assembly, and an energy management system. The front axle power assembly includes a connected engine and a P1 motor. The input end of the three-speed hybrid transmission is connected to the P1 motor via a clutch, and the output end of the three-speed hybrid transmission is connected to the front wheels of the vehicle. The rear axle drive assembly is connected to the rear wheels of the vehicle and includes a P4 motor. This invention's three-speed hybrid drive system, with its dual-motor configuration, enables pure electric rear-wheel drive, series rear-wheel drive, parallel front-wheel drive, and four-wheel drive modes. It can enter parallel operation at 0 speed, and a traction mode is set to ensure that the engine and motor enter parallel operation at low speeds to increase wheel-side torque. By integrating the three-speed transmission with the P1 motor and setting the P4 motor to drive the rear wheels, fuel efficiency is improved while reducing the hybrid gearbox size, simplifying layout, and enhancing feasibility. This invention also discloses a vehicle and a control method for the three-speed hybrid drive system.

The present application relates to the technical field of short-circuit current calculation, and particularly relates to a flexible DC system short-circuit current calculation method based on a physical information neural network, comprising: setting a model input feature vector, the model input feature vector being used to represent a system operating state before a fault and fault information, and performing data preprocessing on the model input feature vector; constructing a hybrid driving calculation model, the hybrid driving calculation model comprising a physical calculation module and a neural network module, and being coupled based on a preset fusion architecture; performing end-to-end training and optimization on the hybrid driving calculation model by using a preset composite loss function; and performing flexible DC system short-circuit current calculation based on the optimized hybrid driving calculation model, so that the problems of poor precision, speed and convergence, poor interpretability and weak generalization ability in the prior art are solved.

The application discloses a kind of based on physical data hybrid drive's dual-fuel ship energy efficiency operation index prediction method, comprising: collecting liquefied natural gas dual-fuel ship historical energy efficiency data and pre-processing;Obtain input characteristics by feature selection.The physical mechanism model is constructed to predict fuel consumption, and the two-way recurrent neural network data-driven model based on the two-stage optimization algorithm framework of bayesian optimization and whale optimization is established, to predict the error of physical mechanism model and historical ship each fuel consumption data;Each fuel consumption predicted based on physical mechanism model is added to the error output by data-driven model through the physical data hybrid drive model formed by the combination of data-driven model and physical mechanism model, to obtain the final each fuel consumption prediction result, and the ship energy efficiency operation index is calculated.The relative error of the energy efficiency operation index of liquefied natural gas dual-fuel ship obtained by the present application can be smaller, to meet the actual energy efficiency management needs.

The application discloses a multi-directional excitation magneto-optical image registration method under hybrid driving, and the method comprises the following steps: collecting the defect magnetic field distribution information of the surface of a ferromagnetic material under multi-directional excitation through a magneto-optical imaging device to obtain a reference image and a floating image; extracting the defect contour of the two images according to a gradient operator; performing spatial transformation on the defect contour of the floating image, and then performing or operation and morphological closing operation on the defect contour of the floating image and the defect contour of the reference image to obtain a defect contour map with closed shape; obtaining the magnetic leakage field distribution map of the defect contour map through defect magnetic leakage field model forward modeling; calculating the similarity measure of the reference image and the magnetic leakage field distribution map generated by the model; and finally continuously updating the spatial transformation parameters through an optimization search algorithm, and the registration is successful when the similarity measure reaches the maximum.

A mining machine having a hybrid diesel-electric drivetrain with launch control includes a diesel engine, an electric generator in communication with the diesel engine to produce electrical power, at least one electric traction motor, at least one brake resistor, and a power controller including a processor. The processor is configured to initiate launch control by receiving an instruction to initiate launch control, increasing electrical power to the at least one brake resistor, increasing power output of the diesel engine to cause the electric generator to produce the increased electrical power, and, upon receiving a launch command, redirecting the electrical power from the at least one brake resistor to the at least one electric traction motor.

The invention provides a self-adaptive fuel heating drive control method and system based on a hybrid vehicle. The method comprises the steps that whether an engine is in a starting operation state or a shutdown state is judged; if the engine is in the starting operation state, detecting the voltage of a small battery and the temperature of fuel oil, and calculating a heating duty ratio according to the voltage of the small battery and the temperature of the fuel oil so as to carry out fuel oil heating driving control; if the engine is in the shutdown state, the current driving mode and the large battery charge capacity are obtained, the driving mode comprises the pure electricity priority mode, the forced pure electricity mode, the oil-electricity hybrid driving mode and the fuel oil priority mode, and according to the current driving mode and the large battery charge capacity, the corresponding heating duty ratio is obtained through calculation and used for fuel oil heating driving control. The optimal fuel oil heating strategy can be determined according to the engine state, the driving mode and the SOC of the hybrid electric vehicle, the fuel oil atomization performance and the fuel oil economical efficiency are improved, and the PN particulate matter emission level of engine operation is improved.

The invention discloses an intelligent interaction management method and system for a coal-fired thermal power plant, and relates to the technical field of coal-fired thermal power plant management, and the method comprises the steps: collecting multi-source heterogeneous data of the coal-fired thermal power plant; generating a structured data asset library; building an intelligent interaction core module; a layered intelligent control system is constructed, a production intelligent control layer performs parameter prediction and closed-loop regulation and control on production core equipment based on production related data in a structured data asset library, and an environmental protection intelligent control layer performs parameter optimization and linkage regulation and control on environmental protection processing equipment through a mechanism-data hybrid drive model based on production data and environmental protection monitoring data. The technical problems that in the prior art, a coal-fired thermal power plant lacks a collaborative management linkage mechanism, so that the operation efficiency is low, the environmental protection standard reaching stability is poor, and the management intelligence level is insufficient are solved, and accurate collaborative management and control of the coal-fired thermal power plant are achieved. And the operation efficiency, the environmental protection standard reaching stability and the management intelligence level of the factory area are improved.