45results about How to "Weak correlation" patented technology

The invention discloses a strawberry greenhouse environment parameter intelligent monitoring system based on a GRNN neural network. The device is characterized in thatthe monitoring system is composedof a strawberry greenhouse environment parameter acquisition platform based on a CAN bus and a greenhouse strawberry yield intelligent prediction system. The strawberry greenhouse environment parameter acquisition platform based on the CAN bus realizes detection, adjustment and monitoring of strawberry greenhouse environment factor parameters. The greenhouse strawberry yield intelligent prediction system comprises a greenhouse strawberry yield prediction subsystem, a strawberry greenhouse temperature prediction subsystem, a strawberry greenhouse moisture prediction subsystem and a greenhousestrawberry yield environmental parameter correction model. The system effectively solves the problem that the strawberry greenhouse yield is predicted according to the characteristics of non-linearity, large lag, complex dynamic change and the like of strawberry greenhouse parameter change in the existing strawberry greenhouse environment, so that the effective management of strawberry greenhouseproduction is greatly influenced.

The invention discloses a green pepper greenhouse temperature intelligent early warning device based on a multi-combination intelligent model. The early warning device is composed of a green pepper greenhouse environment parameter acquisition platform based on a CAN field bus and a green pepper greenhouse temperature intelligent early warning system. According to the device, a plurality of problems of the environment in the closed green pepper greenhouse caused by unreasonable design, backward equipment, incomplete control system and the like in the traditional green pepper greenhouse environment are effectively solved. Moreover, the problems that an existing green pepper greenhouse environment monitoring system does not perform fusion and early warning on the temperature of the green pepper greenhouse environment according to the characteristics of nonlinearity, large lag, large green pepper greenhouse area, complex temperature change and the like of the green pepper greenhouse environment temperature change, so that regulation and control of the green pepper greenhouse environment temperature are greatly influenced are effectively solved.

The invention discloses a cucumber greenhouse yield intelligent prediction device based on a recurrent neural network. The device is characterized in that the prediction device realizes real-time detection of environmental parameters, soil parameters and cucumber greenhouse yield information of a cucumber greenhouse, and the device comprises a cucumber greenhouse parameter detection platform basedon a wireless sensor network and a greenhouse cucumber yield intelligent prediction system; according to the intelligent prediction device for the cucumber greenhouse yield based on the recurrent neural network, the intelligent early warning system detects environmental parameters, soil parameters and cucumber greenhouse yield information of a cucumber greenhouse in real time, and therefore production management of the cucumber greenhouse can be well done, and economic benefits can be improved.

The invention discloses an EMD (empirical mode decomposition) based computation method for recognizing sensitivity of a signal source. The computation method comprises main steps as follows: vibration signals under the normal oil supply parameter and under the adjusted oil supply parameter are set, and a correlation coefficient between the vibration signal under the adjusted oil supply parameter and an IMF (intrinsic mode function) component of the vibration signal is computed; a correlation coefficient between the IMF component and the vibration signal under the normal oil supply parameter is computed; a correlation coefficient difference value generated through oil supply parameter change is computed; the correlation coefficient difference value is subjected to standard processing according to the function, and a sensitivity factor is defined; a sensitive IMF component is selected according to the factor; the sensitive IMF component is subjected to wavelet time-frequency analysis, so that a diesel engine vibration source corresponding to the component is determined. With the adoption of the method, the diesel engine vibration source closely related with the oil supply parameter change is revealed, the recognition precision of the vibration source closely related with the diesel engine oil supply parameter change can be improved, and reasonable selection of the diesel engine oil supply parameter and vibration noise control are realized.

The invention discloses an intelligent detection system for gas concentration. The system is characterized in that the system is composed of a gas environment parameter acquisition platform based on awireless sensor network and a gas multistage early warning module. The gas environment parameter acquisition platform based on the wireless sensor network realizes gas environment parameter detection, gas concentration adjustment and early warning. The gas multistage early warning module is composed of a gas interval number neural network model, an interval number prediction model and an intervalnumber GRNN neural network gas grade classifier. The intelligent detection system effectively solves the problem that the existing gas concentration detection system does not detect the gas concentration according to the characteristics of nonlinearity, large lag, large working condition area, complex gas concentration change and the like of gas change, so that the gas concentration detection accuracy is greatly influenced.

The invention discloses a green pepper greenhouse environment intelligent monitoring system based on a subtractive clustering classifier. The intelligent monitoring system is characterized by comprising a green pepper greenhouse environment parameter detection platform based on a wireless sensor network and a green pepper greenhouse yield intelligent early warning system. In the prior art, only adevice is adopted for monitoring the green pepper greenhouse environment parameter, and green pepper greenhouse yield cannot be warned according to green pepper greenhouse environment temperature andsunlight. The green pepper greenhouse environment intelligent monitoring system solve the above problem.

The invention discloses a cucumber greenhouse temperature intelligent detection device based on an LVQ neural network. The cucumber greenhouse temperature intelligent detection device is characterizedby comprising a cucumber greenhouse environment parameter acquisition platform based on a CAN bus and a cucumber greenhouse temperature intelligent monitoring system. The device effectively solves the problem that the existing cucumber greenhouse monitoring system does not intelligently monitor and predict the temperature of the cucumber greenhouse environment according to the characteristics ofnonlinearity and large lag of the temperature change of the cucumber greenhouse environment, large area and complex temperature change of the cucumber greenhouse and the like, so that the regulation and control of the temperature of the cucumber greenhouse environment are greatly influenced.

The invention relates to the technical field of data processing, and provides a feature screening method and apparatus. The method comprises the steps of obtaining at least two to-be-screened features; determining assessment measurement values corresponding to the features; according to correlation among the features, dividing the at least two features into at least two feature subsets, wherein acorrelation coefficient between a feature corresponding to the maximum assessment measurement value in each feature subset and each of other features is greater than a preset first correlation coefficient threshold; obtaining the feature corresponding to the maximum assessment measurement value from each feature subset, and enabling the obtained features to form an optimal feature set; by calculating an identification probability of the optimal feature set, updating the features comprised in the optimal feature set; and taking the updated features comprised in the optimal feature set as results of screening the at least two features. According to the scheme, feature screening can be performed under the condition of dynamic change of a feature data set.

The invention relates to the field of automatic production, and discloses an air injection control system. An NARX neural network model 1 and an NARX neural network model 2 are used for controlling air flow and predicting an air flow value respectively, and an NARX neural network establishes a dynamic recursive network of the models by introducing a time delay module and outputting feedback, input and output vector delay feedback is introduced into network training to form a new input vector, good nonlinear mapping capability is achieved, the input of the NARX neural network not only comprises the error of original gas flow, the control quantity and the input data of actual gas flow, but also comprises the trained corresponding output data; the generalization ability of the network is improved, so that the system has better prediction precision and adaptive ability in prediction of corresponding parameters of the gas flow compared with a traditional static neural network, and the single-chip microcomputer controller improves the precision, robustness and reliability of the control system.

The invention discloses a strawberry greenhouse temperature intelligent detection device based on an empirical mode decomposition model. The strawberry greenhouse temperature intelligent detection device is characterized by comprising a strawberry greenhouse environment parameter acquisition platform based on a CAN field bus and a strawberry greenhouse temperature intelligent prediction system, the problems that a traditional strawberry greenhouse environment is unreasonable in design, backward in equipment, incomplete in control system and the like, and consequently the environment in the closed strawberry greenhouse still has many problems are effectively solved; moreover, the problems that an existing strawberry greenhouse environment monitoring system does not monitor and predict the temperature of the strawberry greenhouse environment according to the characteristics of nonlinearity, large lag, large strawberry greenhouse area, complex temperature change and the like of the temperature change of the strawberry greenhouse environment, so that the regulation and control of the temperature of the strawberry greenhouse environment are greatly influenced are effectively solved.

The invention relates to a pre-detection active leveling system and leveling method for an agricultural vehicle working platform, and belongs to the technical field of vehicle platform leveling. The pre-detection active leveling system includes a platform, a cross universal joint, a base, a vehicle body, a suspension, wheels, a distance measuring sensor, a vehicle body tilt sensor, a platform tiltsensor, a platform tilt adjustment mechanism, a platform pitch adjustment mechanism and a central processor. In the walking operation process of an agricultural vehicle, the vehicle body tilt sensorperiodically measures the pitch angle and the roll angle of the vehicle body, and the distance measuring sensor periodically synchronous measures relative height information of the ground in front ofthe wheels, and calculates the pitch angle and the roll angle change amount of the vehicle body at the next moment, obtains the linear displacement adjustment amount of the platform tilt adjustment mechanism and the platform pitch adjustment mechanism under the condition of maintaining the platform level, the two adjustment mechanisms complete the predetermined linear displacement adjustment in the adjustment period, and the pre-detection active leveling of the agricultural vehicle working platform during the walking is achieved.

The present invention relates to a phase shift-based pilot frequency design method in a large-scale MIMO system. The method comprises the following steps of 1 establishing an optimization problem by taking the minimum interference sum in a system as a target, and designing a phase shift value of each cell in the system; 2 adopting a discrete particle swarm search algorithm to solve the optimization problem in the step 1 to obtain the phase shift value of each cell; 3 on the basis of the phase shift values obtained in the step 2, adopting a tabu search-based pilot frequency distribution algorithm to obtain a pilot frequency design scheme capable of reducing the pilot frequency pollution. According to the present invention, by establishing and solving the optimization problem of the minimum interference in the system, the correlation of the pilot frequency matrixes between two cells of large mutual interference is weak, and the correlation of the pilot frequency matrixes between two cells of small mutual interference is strong, thereby reducing the pilot frequency pollution. On the basis of the pilot frequency phase shift, by adopting a pilot frequency distribution scheme of low complexity and a tabu search-based pilot frequency distribution method, the channel estimation precision and the frequency spectrum efficiency of the system are improved.

The invention discloses a tension detection and intelligent control system which is characterized in that the control system comprises a parameter acquisition and control platform and an intelligent tension control subsystem, and the control system achieves intelligent control over the winding speed and tension detection of yarn and the tension of the yarn. The reliability and accuracy of yarn winding process measurement and yarn tension control are improved; the method effectively solves the problem that accurate detection and reliable control of the tension detection and control process are greatly influenced due to the fact that an existing tension detection and control process is not influenced by a non-linear uncertain system with large fluctuation, large lag and large fluctuation of the tension in the spinning process.

The invention discloses a livestock and poultry health sign big data Internet of Things detection system which is characterized in that the detection system comprises a parameter acquisition and control platform and a livestock and poultry body temperature big data intelligent prediction subsystem, and accurate detection and prediction of the measured livestock and poultry body temperature are achieved; the system effectively solves the problems that an existing livestock and poultry sign parameter detection system does not accurately detect and predict livestock and poultry sign parameters according to the influence on the livestock and poultry sign parameters due to the fact that the livestock and poultry environment area is large, and the livestock and poultry environment parameters and complex changes such as nonlinearity and large lag of livestock and poultry sign parameter changes are complex; the livestock and poultry health and the livestock and poultry management are greatly influenced.

The invention discloses a pressure big data intelligent detection system based on the Internet of Things, and the system is characterized in that the system comprises a parameter collection platform and a pressure big data processing subsystem, the system achieves the pressure detection and intelligent prediction, and improves the reliability and accuracy of pressure detection and prediction; according to the invention, the problem that accurate detection and reliable management of multi-point pressure are greatly influenced due to the fact that existing pressure detection is not influenced by an uncertain system which is strong in interference, large in lag and nonlinear according to multi-point pressure detection is effectively solved.

The invention discloses a big data detection system for livestock and poultry activity information. The big data detection system is composed of a livestock and poultry sign parameter acquisition and intelligent prediction platform based on a cloud platform and a livestock and poultry activity big data prediction subsystem. The livestock and poultry physical sign parameter acquisition and intelligent prediction platform based on the cloud platform is used for detecting and processing temperature and activity information parameters of livestock and poultry physical signs; the livestock and poultry activity big data prediction subsystem is used for predicting the activity state of livestock and poultry and providing data and early warning for preventing livestock and poultry diseases; the invention aims to provide the big data detection system for the livestock and poultry activity information, and the system monitors the livestock and poultry body temperature and activity information in real time, so that data and early warning are provided for preventing livestock and poultry diseases.

The invention discloses an environment big data Internet of Things intelligent detection system, which comprises an environment parameter acquisition platform and a formaldehyde big data intelligent prediction subsystem, realizes accurate detection and grade classification of formaldehyde concentration, and improves the reliability and accuracy of formaldehyde concentration detection. The system effectively solves the problems that an existing environment parameter detection system has no influence on the accuracy and reliability of measured environment parameters according to complex changes such as large environment area, nonlinearity of environment parameter changes, large lag and the like, and does not accurately detect and predict the environment parameters, so that the monitoring and management of the environment parameters are greatly influenced.

The invention discloses a material weighing large data detection and packaging intelligent control system which is characterized in that the system comprises a parameter acquisition and control platform and a packaging intelligent control subsystem, and intelligent control over the weight detection and packaging process of packaged materials is achieved. The reliability and accuracy of weighing and packaging in the material packaging control process are improved; the method effectively solves the problem that accurate operation and reliable management of the material weighing and packaging process are greatly affected due to the fact that the existing material weighing and packaging process does not have the influence of a non-linear uncertain system with strong interference and large lag in the material weighing and packaging process on accurate weighing and reliable packaging of the material.

The oil gas concentration big data intelligent detection system is composed of a measurement parameter acquisition platform and a multi-point oil gas concentration prediction subsystem, the measurement parameter acquisition platform achieves accurate detection of measurement parameters, and the multi-point oil gas concentration prediction subsystem achieves processing of the measurement parameters and oil gas concentration prediction. The accuracy and reliability of oil gas concentration detection are improved; the method effectively solves the problem that the reliable operation and intelligent management of safe production of oil and gas enterprises are greatly influenced due to the fact that the existing oil and gas concentration is not influenced by nonlinearity, large lag, coupling and the like of oil and gas concentration change and the oil and gas concentration is not accurately predicted.