291results about How to "Solve nonlinear problems" patented technology

The invention provides a non-contact free space eye-gaze tracking method suitable for man-machine interaction. The method comprises the steps of positioning and tracking a face and eyes in real time, extracting eye movement biological characteristic information, building an eye movement model based on the eye movement biological characteristic information, building a mapping relational model of the eye movement model and an eye-gazed object and the like. The non-contact free space eye-gaze tracking method suitable for the man-machine interaction relates to multiple crossed fields of image processing, computer vision, pattern recognition and the like and has wide application prospect in the fields of new-generation man-machine interaction, disabled people assisting, aerospace relating field, sports, automobile and airplane driving, virtual reality, games and the like. In addition, the method has great practical significance for improvement of the life and self-care level of disabled people, building of a harmonious society and improvement of independent innovative capability in the national high-and-new technical fields of the man-machine interaction, unmanned driving and the like.

The invention provides a control scheme of a VAV (variable air volume) air-conditioning system. A neural network predictive control method is used for a tail end VAV-BOX and an air-conditioning unit, thus the hysteresis characteristic of a VAV system can be overcome, the control accuracy is improved, the resonance phenomena of an actuating mechanism can be greatly reduced, the energy-saving effect is improved by above 13%, and the control parameter tuning problem in the project is solved. A pressure independent cascade stage predictive control method is used for the tail end VAV-BOX, thus thecontrol accuracy can also be improved. The air-conditioning unit is provided with four control loops and can automatically select a static pressure control or total air volume control policy with adjustable setting static pressure by using an all-condition integrated control technology, thus the predictive control on a fan can be realized; the primary air volume of all the tail end VAV-BOXes can be detected and the air supply temperature can be adjusted according to the operation condition, thus the problem of too low temperature in partial air-conditioning area in the lowest fresh air operation is solved; the cascade stage predictive control method is adopted for a fresh air ratio control loop, thus the accurate control on the fresh air ratio can be realized and the energy-saving level can be further improved.

The invention discloses a traversal control system and method of a driverless automobile based on a hybrid theory. The system comprises a sensing module, a path planning module, a hybrid controller module, a steering executing module and a display module. The sensing module is used for acquiring the vehicle travelling environment and the vehicle operating state; the path planning module is used for planning a path based on the absolute position of a vehicle in a map and the position of the vehicle relative to peripheral obstacles and a lane line; the hybrid controller module comprises a local controller unit and a switching monitoring controller unit; the local controller unit designs a controller meeting a target for working conditions of lane-keeping, lane conversion and emergent collision avoidance; the switching monitoring controller unit driven by different discrete events drives effective mode switching, so that the switching stability is guaranteed; the steering executing module drives a steering motor according to a signal of the hybrid controller so as to realize automatic steering of the driverless automobile; and the display module is used for displaying sensing information, path planning information and control mode information.

The invention discloses a product order prediction method and device with time series characteristics. The product order prediction method comprises obtaining statistics of order data of enterprises at every time point according to stored historical order data; selecting a prediction model according to the time series characteristics of the order data and determining a prediction output equation of the prediction model; enabling the statistics of the historical order data to be processed as a prediction input table according to the requirements of the prediction model and training a corresponding prediction network model; and utilizing the prediction input table of the prediction order quantity to calculate to obtain the prediction order quantity of orders according to the prediction model which is well-trained through prediction orders and the prediction output equation. The invention also provides the product order prediction device according to the product order prediction method. The product order prediction device mainly comprises a data acquisition module, a data preprocessing module, a time series modeling module and an order prediction module. The product order prediction method and device with the time series characteristics have the advantages of solving the nonlinear problem of product order prediction, meeting the requirements of system real-time performance and improving the prediction accuracy.

The invention discloses a single lithium ion battery SOC estimation method based on sliding window filtering. In a novel algorithm, a battery model is composed of two RC parallel circuits, one series resistor and one nonlinear voltage source, the dynamic working state in a battery is simulated through battery terminal voltage, the RC parallel circuits and a battery SOC. The single lithium ion battery SOC estimation method is based on an electrochemistry-circuit equivalent lithium ion battery combination model, the model well describes the nonlinear function relation between battery OCV and the battery SOC, and the SMO algorithm is used for solving the nonlinear problem of the model. Meanwhile, in the single lithium ion battery SOC estimation method, the SMOS algorithm and the Kalman filtering algorithm are innovatively combined to solve the problem of uncertainty of a lithium ion battery model, and the accuracy of the battery model and the reliability of a battery control system are guaranteed. At last, the battery model parameter on-line identification method provides necessary parameter values for on-line accurate estimation of the battery SOC.

The invention discloses a dual bootstrap and voltage compensation technology-based A / D converter sampling switch, which comprises a primary switch unit used for a to-be-sampled signal channel to sample to-be-sampled signals, a underlayer voltage bootstrap unit for realizing the underlayer voltage bootstrap of a switching tube PMOS Switch in the primary switch unit, a grid voltage bootstrap unit for realizing the grid voltage bootstrap of the switching tube PMOS Switch in the primary switch unit, a storage unit for parallelly sampling input signals VIN and realizing the temporary storage of a VIN voltage, and a voltage compensation unit for compensating the sampling output voltage of an output end VOUT. The invention provides a sampling switch which is capable of working at low voltage and low power consumption and is insensitive to process errors. Meanwhile, the adopted voltage self-compensation method effectively solves a nonlinear problem caused by clock feedthrough occurring after the grid voltage bootstrap of the switching tube.

The multi-model dynamic matrix feeding amount control method for coagulation system in waterworks features that dynamic matrix predicting controller is constituted based on comprehensive multi-model dynamic matrix control theory and by means of using raw water flow rate as the control target, separating the whole system into several subspaces and separate experiments to obtain sectional linear models of the subspaces; and that outflow water turbidity is controlled through determining the dynamic model of the system, predicting the outflow water turbidity, weighting treatment to obtain overall control increment and regulating the feeding amount based on the current work conditions, practical water flow rate and certain weighting police. The present invention can ensure smooth outflow water turbidity, system running economic performance, high robustness and low cost.

The invention discloses a star sensor and gyro combination attitude determination method based on SR-UKF(extended Kalman filter) filtering, and belongs to the technical field of high-precision combined attitude determination of cartographic satellite or other space vehicles. The invention aims at providing the star sensor and gyro combination attitude determination method based on SR-UKF filtering. An SR-UKF filtering algorithm is used for star sensor and gyro combination attitude determination; great improvement is realized on the existing conventional EKF filtering method. The method concretely comprises the following steps of 1, simulating the star sensor quaternion number and gyro angle speed; 2,using the error quaternion number and gyro random drift error as state variables; using theSR-UKF algorithm for realizing the fusion processing of the star sensor and gyro attitude information for filtering processing; performing feedback; possibly eliminating the error influence of the star senor and gyro error influence through iteration wave filtering processing; solving high-precision attitude information.

The invention belongs to the technical field of image processing, and particularly provides a hyperspectral image classification method based on a nuclear low-rank representing graph and spatial constraint. The method includes the implementing steps that (1), samples of all known labels of a hyperspectral image serve as training samples, samples of unknown labels serve as test samples, and a sample set is constructed according to the sequence that the training samples are located in front of the test samples; (2), column normalization is conducted on the sample set and nuclear space mapping is conducted; (3), low-rank representation is conducted on the sample set obtained after nuclear space mapping, so that the nuclear low-rank representing graph is acquired; (4), a space information graph of the sample set is established; (5), the low-rank representing graph and the space information graph are summed, so that a new graph is established; (6), according to a graph maintaining standard method, category labels of the test samples are acquired. The method mainly overcomes the defect that the classification accuracy is low when training samples are insufficient in an existing method, meanwhile, the features of the hyperspectral image are reasonably considered, a better classification result can be acquired in the combination of space information, and the better robustness and the higher accuracy are achieved by the adoption of a nuclear low-rank method.

The invention discloses an attitude control type direct lateral force and aerodynamic force composite missile attitude control method based on mixed forecasting control, belongs to the field of aircraft control, and solves the problem that a nonlinear characteristic and a control input mixing characteristic of a model cannot be simultaneously solved by an existing attitude control method. The attitude control type direct lateral force and aerodynamic force composite missile attitude control method is characterized by comprising the following steps: constructing a complete direct lateral force and aerodynamic force composite missile attitude control model and a direct lateral force model, and converting a nonlinear kinetic model into a piecewise affine model by analyzing an aerodynamic characteristic; constructing a composite control missile mixing logic dynamic model by considering the mixing characteristic of a control input according to the equivalence property of the piecewise affine model and the mixing logic dynamic model; designing an explicit model forecasting control rule based on the mixing logic dynamic model so as to determine an aerodynamic steering engine control rule and an attitude control engine starting rule. The method disclosed by the invention is suitable for being used in the field of aircraft missile control.

The invention discloses a smart energy management device of a multi-energy power supply system. The smart energy management device of the multi-energy power supply system comprises a photovoltaic power generation device, a wind power generation device, a diesel generator device, an energy storing device, an alternating load, a direct current bus device, an alternating current bus device, a smart controller device, a rectifier, a detection device and an inverter device. The multi-energy power supply system integrates solar energy, wind energy, a diesel generator and a storage battery device, has strong nonlinearity, and has a certain degree of inertia. According to the smart energy management device of the multi-energy power supply system, fuzzy control algorithm is used for integrated management of energy. The smart energy management device is characterized by intelligent control, effectively solves the nonlinear problem of the system, enables overshoot of the system to be reduced, even enables overshoot not to occur, and enables the system to have strong robustness and adaptivity.

The invention belongs to the technical field of evaluation of components in mud shale, and discloses a method of evaluating volumes of components in mud shale by means of a conventional logging curve. The method includes the steps of: 1) on the basis of organic carbon analysis, porosity test and total rock inspection test of the mud shale after extraction, with combination of densities of the components in the mud shale, calibrating the volumes of the components in the mud shale and establishing a component volume model of the mud shale; 2) on the basis of evaluation of total organic carbon content with a [delta]logR method, with combination of a relationship of organic carbon before and after the extraction, calculating volume of kerogen, and performing optimizing calculation to obtain a BP neural network model of mineral components and pore volumes in a cross validation manner. The method, on the basis of ensuring that the sum of the volumes of the components in mud shale is 1, achieves the advantages of multiple inputs and multiple outputs of the BP neural network and solves a complex nonlinear problem between the components in mud shale and logging response.

The invention discloses a non-linear time-varying process fault monitoring method based on high efficiency recursion kernel principal component analysis and belongs to the fault detection and diagnosis technology field. The method comprises steps that data having non-linear and slow time-varying characteristics and containing faults is acquired from a Tennessee Eastman process simulator, a Gauss kernel function is utilized to project the acquired normal data to the high-dimensional characteristic space and is centralized, an initial offline monitoring model is established, and a kernel densityestimation function is employed to determine control limit; secondly, when new process data is acquired, through introducing a first-order interference theory method, a model is directly updated based on a characteristic value and a characteristic vector acquired in the offline model, the new data is projected to the updated kernel space and the residual error space to calculate T2 and SPE statistics; when the corresponding control limit is surpassed, occurrence of a monitoring fault is determined, otherwise, the whole process operates normally. The method is advantaged in that two problems are mainly solved, 1), a problem of relatively high false alarm rate generated during fault monitoring in the non-linear time-varying process of kernel principal component analysis is solved; and 2), aproblem of relatively high load existing in a recursion algorithm based on characteristic constant decomposition is solved.

The invention discloses an integrated attitude determination method based on an ant colony unscented particle filter algorithm, which relates to an inertial / astronomical integrated attitude determination method. The method comprises the following steps of: compensating gyro output data by using inertial measurement information and acquiring carrier attitude information through attitude calculation; acquiring required astronomical attitude information by using astronomical measurement information through a deterministic algorithm; fusing the astronomical attitude information with the carrier attitude information by using an ant colony unscented particle filter algorithm, solving nonlinear and noise non-Gaussian problems of a system, solving high-accuracy carrier attitude information, estimating gyro drift, and feeding back and correcting carrier attitude and compensating the gyro drift; and finally realizing on-line correction of eliminating random errors of a gyro of an inertial / astronomical integrated navigation system in real time based on the astronomical measurement information, and finishing long-term and high-accuracy combined attitude determination of a spacecraft.

The invention discloses a support vector machine high-voltage circuit breaker fault diagnosis method based on fuzzy clustering. The method comprises the steps of firstly, extracting a feature vector from a stroke-time curve of a circuit breaker moving contact, using the feature vector as a database of fault diagnosis, secondly, conducting fizzy clustering processing on a data sample, generating a new clustering center matrix, thirdly, using the clustering center matrix as a training sample, applying a support vector machine to conduct training, and fourthly applying the high-voltage circuit breaker fault diagnosis method to diagnose test data. According to the support vector machine high-voltage circuit breaker fault diagnosis method based on the fuzzy clustering, the efficiency of the high-voltage circuit breaker fault diagnosis can be effectively improved, the time of the fault diagnosis is reduced, the quality of the fault diagnosis is improved, and the support vector machine high-voltage circuit breaker fault diagnosis method has great significance for research on the safety and the reliability of the power grid.

The invention discloses a navigation control method of a medical capsule endoscope and solves the problem of external active control on the capsule endoscope when the permanent magnet capsule endoscope performs internal examinations. The method that a permanent magnet and an electromagnetic coil are combined to control internal stable suspension motion of the capsule endoscope is adopted. The control method comprises links including a sensor signal processing link, a feedforward control law link, a feedback control law link, a state estimator link and the like. In the sensor signal processing algorithm link, position information of the capsule endoscope is acquired from an original signal returned by a sensor; in the feedforward control law link, a theoretical output signal value at the steady state is calculated according to a theoretical model to serve as a basic output signal so as to reduce the system nonlinear effect; in the feedback control law link, closed-loop control is realized according to feedback position information to reduce system errors; the state estimator link is used for eliminating or reducing instability caused by system pure lag. With the adoption of the method, stable constraint of any position of the capsule endoscope in liquids in body cavities by an external device can be realized finally.

The invention discloses a soft measurement method of quality index data in the rubber mixing process of an internal mixer. The method is characterized by establishing a mixing process off-line model based on the historical data of the rubber mixing process, then acquiring the production data and the quality index data of the current mixing process as the current samples and projecting the current samples to high dimensional feature space, computing the linear relationship between the samples in the high dimensional feature space by the Kernel function, correcting the parameters and the samples of the model according to the linear similarity of the samples, eliminating the data samples with least contribution when the sample number in the model exceeds the set value, updating the mixing processing model on line and outputting the predicated value of the quality index of the mixed rubber in real time when the current production data are input, thus realizing soft measurement in the rubber mixing process. The method realizes effective online tracking of the rubber mixing process, is low in computation complexity, good in stability and accurate and reliable in measurement and has quite important practical value in pre-detection and system optimization control in the rubber mixing production process.

The present invention relates to an electrical impedance tomography (EIT) method based on deep learning, and is applied to the technical fields of medical imaging, industrial process image and geological prospecting, etc. The method comprises: obtaining an original boundary measurement voltage sequence and a conductivity distribution sequence, and performing normalization processing to obtain a training sample set; establishing an initial EIT deep learning network model, and training the EIT deep learning network model according to the training sample set and the setting training mode to allow the EIT deep learning network model obtained through training to represent the mapping relation between the original boundary measurement voltage sequence and the conductivity distribution sequence; and inputting the boundary measurement voltage sequence to the mapping relation to obtain the conductivity distribution sequence, and finally recovering the conductivity distribution sequence to a matrix mode to obtain an EIT image. The electrical impedance tomography method based on deep learning simplifies the modeling process and the solution difficulty of problems so as to solve the nonlinearity and ill-conditioned problems when the electrical impedance inverse problem is solved and improve the solution precision of the inverse problem and the image reconstruction quality.

The invention discloses an improved Gaussian mixed potential probability hypothesis density filtering method. The method comprises the following steps: 1) forming a target state set and a target strength function; 2) initializing probability hypothesis density and potential distribution of an initial target; 3) carrying out predication on the probability hypothesis density and potential distribution of the target state set at the time of k+1 to obtain probability hypothesis density and potential distribution at the time of k+1; 4) updating the probability hypothesis density and potential distribution of the target state set at the time of k+1 to obtain probability hypothesis density and potential distribution at the time of k+1, carrying out unbiased conversion on a true covariance matrix and true deviation, and setting an ellipsoid threshold value to simplify a measurement set and reduce observation number of a current observation set; 5) carrying out trimming and combining on Gaussian items of the target strength function, and extracting target state estimation and carrying out performance evaluation; and 6) repeating the steps 3)-5), and tracking the target until the target disappears. The method facilitates direct application of radar data information, and reduces calculation amount of a filter.

The invention discloses a pedestrian tracking method based on a low-altitude aerial photographing infrared video. Continuous and stable pedestrian tracking is realized through combination of a Lucas-Kanade optical flow method and local area secondary detection. The pedestrian tracking method comprises the steps that 1. an aerial photographing infrared pedestrian support vector machine (SVM) classifier is trained offline; 2. the initial position of a pedestrian target is determined; 3. the pedestrian target is preliminarily tracked by utilizing the LK optical flow method, and the position of the pedestrian target in the next frame is calculated; 4. a search area is set around the predicted position of the pedestrian target; and the infrared pedestrian is secondarily detected in the search area by utilizing the offline trained SVM classifier, and the position of the pedestrian target is updated; and 5. the center of the pedestrian target detected in the search area acts as input coordinates of the LK optical flow method of the next time, and the steps (3)-(5) are repeated. Continuous and stable tracking of the infrared pedestrian target can be realized by the pedestrian tracking method, and the problem of street lamp shielding can also be processed.

The invention discloses a strain type linear bidirectional large displacement sensor and a detection method of the strain type linear bidirectional large displacement sensor. According to the strain type linear bidirectional large displacement sensor, a strain sheet is arranged on a displacement strain conversion beam to form a bridge circuit which is connected with a strain gauge, a fixed end of the displacement strain conversion beam is fixed on a detection point, and a freed end of the displacement strain conversion beam is inserted into a rigid guide groove and fixed at a reference position. According to the detection method of the strain type linear bidirectional large displacement sensor, when the detection point moves, the displacement of the rigid guide groove is in linear relation with the strain at the position of the strain sheet on the displacement strain conversion beam, strain values during the movement of the detection point are recorded through the strain gauge, and the displacement value of the detection point is determined through the recorded strain values according to a marked relational expression between the strain and the displacement. The strain type linear bidirectional large displacement sensor is simple in structure, good in repeatability, high in sensitivity, convenient to manufacture and install, low in cost, and suitable for detection and tests in an engineering structure, especially the measurement of large displacement of the engineering structure and components of the engineering structure.

The invention relates to a penicillin fermentation process failure monitoring method based on recursive kernel principal component analysis (RKPCA), which belongs to the technical field of failure monitoring and diagnosis. The method comprises the following steps: acquiring the ventilation rate, stirrer power, substrate feed rate, substrate feed temperature, generated heat quantity, concentrationof dissolved oxygen, pH value and concentration of carbon dioxide; and establishing an initial monitoring model by using the first N numbered standardized samples, updating the model by a RKPCA method, and computing the characteristic vectors to detect and diagnose the failure in the process of continuous annealing, wherein when the T2 statistics and SPE statistics exceed the respective control limit, judging that a failure exists, and otherwise, judging that the whole process is normal. The method mainly solves the problems of data nonlinearity and time variability; and the RKPCA method is used for updating the model by carrying out recursive computation on the characteristic values and characteristic vectors of the training data covariance. The result indicates that the method can greatly reduce the false alarm rate and enhance the failure detection accuracy.

The invention discloses a fetus electrocardiosignal extracting method based on a self-adaptation FLANN filter, and belongs to the technical field of fetus electrocardiosignal detecting. The fetus electrocardiosignal extracting method solves the problem that due to the nonlinearity of a mother electrocardiosignal from the chest area to the abdomen area and the non-stability characteristic of the electrocardiosignal , the fetus electrocardiosignal is extracted inaccurately. A mother and fetus mixed electrocardiosignal is obtained through the lead of the abdomen of the mother, and the mother electrocardiosignal is obtained through the lead of the chest of the mother; two kinds of electrocardiosignals obtained from the body surface of the mother are respectively processed; the preprocessed mother and fetus mixed electrocardiosignal and the preprocessed mother electrocardiosignal are normalized to serve as an original input signal and a reference input signal of the self-adaptation FLANN filter; parameters of the self-adaptation FLANN filter are constantly updated through the self-adaptation LMS algorithm, and the non-linear relationship of the mother electrocardiosignal from the chest area of the mother and the abdomen area of the mother is estimated; an error output signal of the self-adaptation FLANN filter serves as an estimated fetus electrocardiosignal. The fetus electrocardiosignal extracting method is used for extracting the fetus electrocardiosignal.

The invention relates to a method and a system of failure prediction. The method of the failure prediction comprises calculating a kernel principal element, and detecting a failure according to control limits. A method based on kernel principal component analysis (KPCA) reconfiguration is adopted in the failure prediction aiming at rotating machinery, the nonlinear problem of process data can be solved well, a failure direction is obtained from the data implying failures, a failure amplitude value is estimated, the multi-dimensional character of the failures is considered, and an accurate failure prediction result can be obtained.

The invention discloses a multi-region electricity-gas coupling comprehensive energy system optimal scheduling method considering tiered gas prices. The method comprises the following steps: S1, a typical combined cooling heating and power supply system are adopted in an energy supply mode of a comprehensive energy system in a region, power-to-gas equipment and gas storage equipment are arranged to consume new energy to achieve energy standby application, and a gas bus is added for constraint modeling; S2, energy connection is achieved through coupling interconnection of electric energy and natural gas between regions, the natural gas serves as main complementary energy, and an electricity-gas steady-state coupling linear power flow algorithm is completed in combination with a second-ordercone relaxation and incremental linearization method; S3, based on the electricity-gas steady-state coupling linear power flow, a double-layer linearization algorithm is adopted to solve the nonlinear problem of a compressor gas consumption model; and S4, a traditional day-ahead economic dispatching target function is optimized and perfected, the tiered gas price is considered to achieve optimaldispatching by taking the minimum total operation cost of all parks as a target through energy interconnection distribution among different functional areas, namely a residential area, a commercial area, an office area, an industrial area and the like.

The invention relates to a rotor levitation centre determination method for a permanent magnet motor-driven maglev molecular pump. A testing rotor for eliminating the magnetic field of permanent magnet motor rotor steel magnets is utilized, and by means of a method which adjusts the levitation centre of the testing rotor to guarantee the current equilibrium of each magnetic pole of magnetic bearings, the levitation centre of a rotor of the maglev molecular pump is obtained. The testing rotor eliminates the affection of the magnetic bias pull of the rotor of a permanent magnet motor, and the levitation centre of the rotor of the maglev molecular pump can be accurately obtained.

The invention discloses a variable optical attenuator-based bus current detection method and current transformer equipment. The method comprises the following steps: generating monitoring light and sensing light by a monitoring light source and a sensing light source respectively, wherein the sensing light passes through a variable optical attenuator to generate an attenuated optical signal, is subjected to photoelectric conversion with the monitoring light respectively and then is input into a single-chip microprocessor for algorithmic processing so as to generate a secondary current signal; meanwhile, acquiring a bus current signal on a high-voltage line by using a primary current sensor, and differentiating, filtering and amplifying with feedback electric signals of an input signal and an output signal of the variable optical attenuator to generate a new driving signal for driving the variable optical attenuator. The equipment comprises the monitoring light source, the sensing light source, the variable optical attenuator, two wavelength division multiplxers, two photoelectric receivers, the single-chip microprocessor and a photoelectric feedback control module, wherein the photoelectric feedback control module comprises two photoelectric receivers, a differential module, a filter circuit and an amplifying circuit.

The invention provides a tire pressure monitoring system. The tire pressure monitoring system comprises a display alarming unit, vibration sensors, wheel speed sensors and a central processing unit, wherein the vibration sensors and the wheel speed sensors are connected to wheels; the central processing unit is used for storing stable vibration frequency signals of all the wheels under various conditions when the car moves and taking the stable vibration frequency signals as a rule base of vibration frequency data of the car; the central processing unit is used for taking vibration frequency signals and wheel speed signals, which are measured in real time, as input variables and inputting the input variables into a fuzzy controller to be calculated to obtain a difference value of stable-state data in a real-time vibration frequency signal and wheel speed signal rule base; and when the difference values of the vibration frequency signals and the wheel speed signals are more than a pre-set threshold value, the central processing unit transmits an alarming signal to the display alarming unit. The tire pressure monitoring system provided by the invention can be used for monitoring a tire pressure state through the measured vibration frequency signals and wheel speed signals of all the wheels; and the vibration frequency signals and the wheel speed signals are monitored at the same time and the judgment accuracy is high.

The invention discloses a method and device for controlling an air conditioner, the air conditioner, a storage medium and a processor. The method comprises the steps that whether the absolute value ofthe difference value between the current indoor environment parameters and the target indoor environment parameters of the air conditioner is larger than a set threshold value or not is determined; if the absolute value is larger than the set threshold value, the air conditioner is controlled to work at a first work mode, so that the absolute value of the difference value between the current indoor environment parameters and the target indoor environment parameters of the air conditioner is reduced through first adjustment on the current indoor environment parameters; and if the absolute value of the difference value is smaller than or equal to the set threshold value, the air conditioner is controlled to work at a second control mode, so that the absolute value of the difference value between the current indoor environment parameters and the target indoor environment parameters is maintained through second adjustment on the current indoor environment parameters. According to the scheme, the problem that the development process of the air conditioner depends on manual experience too much, and the energy efficiency ratio of the air conditioner cannot be adjusted can be solved, andthe effect of increasing the energy efficiency ratio of the air conditioner is achieved.

The invention discloses a power load probability density prediction method based on fuzzy support vector quantile regression. According to the method, maximum day load data and average temperature data before a prediction day are collected; a train set and a test set are established through adoption of history data; lagrangian multipliers and support vector subscripts of a fuzzy support vector quantile regression model are obtained through utilization of the train set; the fuzzy support vector quantile regression prediction model is established according to obtained model parameter values; the test set is substituted into the model to obtain prediction values; and probability density prediction of the maximum day load is realized through utilization of the obtained prediction values under different quantiles and through application of kernel density estimation. According to the method, prediction errors can be effectively reduced, the power load prediction precision is improved, the good prediction effect is obtained, and the relatively reliable basis is provided for a power system scheduling department to adjust power consumption plans and optimize generator set contribution.