31results about How to "To achieve the purpose of dynamic modeling" patented technology

The invention discloses a DRNN neural network-based eggplant greenhouse temperature intelligent detection device. The detection device is characterized in that the detection device is composed of a CAN bus-based eggplant greenhouse environment parameter acquisition platform and an eggplant greenhouse temperature intelligent detection system. An existing eggplant greenhouse monitoring system failsto detect the temperature of the greenhouse environment of eggplants according to characteristics such as the non-linearity and large lag of the temperature change of the environment of an eggplant greenhouse and complex temperature change due to the large area of the eggplant greenhouse, and as a result, the adjustment and control of the temperature of the environment of the eggplant greenhouse is greatly affected, while, with the DRNN neural network-based eggplant greenhouse temperature intelligent detection device adopted, the above problem can be solved.

The invention discloses a multi-point temperature sensor intelligent monitoring system based on a field bus network, and the system consists of an apple orchard environment parameter collection platform and an apple orchard environment temperature evaluation subsystem, and achieves the intelligent detection of the apple orchard environment temperature and the evaluation of the temperature. The system effectively solves the problem that the existing apple orchard environment monitoring does not intelligently monitor and predict the temperature of the apple orchard environment according to the characteristics of nonlinearity, large lag, large area of the apple orchard, complex temperature change and the like of the apple orchard environment temperature change, so that the monitoring of the apple orchard environment temperature is greatly influenced.

The invention discloses an intelligent detection system for an eggplant greenhouse environment based on a wavelet neural network. The detection system comprises two parts: an eggplant greenhouse environmental parameter acquisition platform based on a wireless sensor network and an eggplant greenhouse yield intelligent early warning system. The system effectively solves the problems that the conventional eggplant greenhouse yield is affected by nonlinearity and large hysteresis of eggplant greenhouse environment temperature changes, large area of the eggplant greenhouse, complicated temperaturechange and the like, the eggplant greenhouse yield is not predicted and the eggplant greenhouse environment temperature is not accurately detected and adjusted, so that the prediction of the eggplantgreenhouse environment yield and the production management are greatly affected.

The invention discloses a highway pavement use performance detection system. The detection system is composed of a pavement meteorological environment parameter acquisition platform based on a CAN busand a highway pavement use performance grade classification system. The pavement meteorological environment parameter acquisition platform based on the CAN bus realizes detection and adjustment of pavement meteorological environment factor parameters, and the highway pavement use performance grade classification system realizes prediction and classification of highway pavement use performance grades. The system effectively solves the problems that an existing highway pavement evaluation system does not accurately detect pavement meteorological environment parameters according to the characteristics of nonlinearity, large lag, complex meteorological environment parameter changes and the like of pavement meteorological environment parameter changes, and therefore comprehensive evaluation ofpavement use performance is greatly affected.

The invention discloses a multipoint fire early warning system based on the Internet of Things. The system is composed of a gas station oil tank area environment parameter acquisition platform based on a ZigBee network and a gas station oil tank area environment multipoint fire early warning subsystem. The gas station oil tank area environment multi-point fire early warning subsystem is composed of parameter detection units and HRFNN fuzzy recurrent neural network fire early warning classifiers. The system effectively solves the problem that an existing gas station oil tank area environment monitoring system is incapable of accurately detecting environment parameters and raising early warning against a fire hazard according to characteristics of nonlinearity, large lag and complexity of environment parameter change of the gas station oil tank area, and thus improves the accuracy of predicting the fire hazard parameters and fire hazard of the gas station.

The invention discloses an intelligent orchard yield prediction system based on Internet of Things. The intelligent orchard yield prediction system consists of an apple orchard environment parameter collection platform and an apple orchard environment yield prediction subsystem, and achieves the detection of apple orchard environment microclimate parameters and the prediction of the yield. The apple orchard environment monitoring system effectively solves the problems that an existing apple orchard environment monitoring system does not accurately detect the apple orchard environment temperature and predict the yield according to the characteristics of nonlinearity, large lag, complex change and the like of the apple orchard environment temperature change, so that the accuracy and robustness of predicting the apple orchard environment temperature and yield are improved.

The invention discloses an intelligent monitoring system for multiple oil and gas concentration sensors based on Internet of things. The system consists of a gas station tank area environmental parameter acquisition platform based on a ZigBee network, and a gas station tank area environmental oil and gas concentration sensor monitoring subsystem. Through the adoption of the intelligent monitoringsystem, the problems that the gas station tank area environmental oil and gas concentration is not detected accurately and that an early warning is not given according to the characteristics of non-linearity, large lag and complex change of the gas station tank area environmental oil and gas concentration change in an existing gas station tank area environmental monitoring system is effectively solved, and the gas station oil and gas concentration prediction accuracy and robustness are improved.

The invention discloses an intelligent monitoring system for water quality of a fishpond. The intelligent monitoring system is characterized by being composed of two parts, namely a fishpond water quality parameter detection platform based on a wireless sensor network as well as a fishpond yield intelligent prediction system. The intelligent monitoring system disclosed by the invention effectivelysolves the problems that fishpond water quality parameters are monitored only by virtue of equipment in the prior art, only the fishpond water quality parameters are acquired and yield of the fishpond can not be effectively predicted according to influence of water quality of the fishpond on the yield of the fishpond.

The invention discloses an intelligent building energy consumption detection system. The invention is characterized in that the system is composed of a building energy consumption parameter acquisition platform based on a wireless sensor network and a building energy consumption grade classification system. The building energy consumption parameter acquisition platform based on the wireless sensornetwork realizes detection and monitoring of parameters influencing the building environment. The building energy consumption grade classification system is composed of a temperature detection module, an illuminance detection module, a humidity detection module, an energy consumption prediction module and an interval number kohonen neural network building energy consumption state classifier. Theexisting building energy consumption is not intelligently detected according to the characteristics of nonlinearity, large lag, complex influence on building energy consumption change and the like ofbuilding environment factor changes, so that the building detection accuracy is greatly influenced; said problems can be solved by using the system of the invention.

The invention discloses an aquaculture pond dissolved oxygen detection device. The device is characterized in that a detection system is composed of two parts including an aquaculture pond environmentwater quality parameter acquisition platform based on a wireless sensor network, and a pond dissolved oxygen intelligent early warning system. The device provided by the invention effectively solvesa problem of great influence on aquaculture pond dissolved oxygen detection caused by lack of detecting the dissolved oxygen of the existing aquaculture pond according to the characteristics, such asnonlinearity and great lag of change of pound dissolved oxygen and large area and complex change of dissolved oxygen of the aquaculture pond.

The invention discloses a dust concentration intelligent detection system which is composed of a dust concentration environmental parameter acquisition platform based on a CAN bus and a dust concentration intelligent prediction module. The dust concentration environmental parameter acquisition platform based on the CAN bus realizes detection and adjustment of dust concentration environmental factor parameters. The dust concentration intelligent prediction module is composed of a dust concentration interval number neural network model, an interval number prediction model and an interval numberCMAC cerebellar neural network dust concentration fusion model. The system effectively solves the problems that the existing industrial and agricultural production environment does not have the characteristics of nonlinearity, large lag, complex dynamic change and the like according to the change of the dust concentration, and the dust concentration cannot be accurately detected and predicted, sothat the effective management of the dust concentration of the industrial and agricultural production environment is greatly influenced.

The invention discloses an intelligent oil gas concentration monitoring system based on a field bus network. The system is composed of a gas station oil tank area environment parameter acquisition platform based on a CAN field bus network and a gas station oil tank area environment multi-point oil gas concentration leakage classification subsystem. The system realizes intelligent detection of theoil gas leakage concentration of the gas station oil tank area environment and classification of the oil gas leakage concentration level. The system not only effectively solves many problems existingin the oil gas concentration detection of the gas station oil tank area environment caused by unreasonable design of the oil gas concentration detection equipment, backward equipment, incomplete detection system and the like of the traditional gas station oil tank area environment oil gas concentration detection device, but also effectively solves the problem that the existing gas station oil tankarea environment monitoring system detects and classifies the concentration of the gas station oil tank area environment, thereby greatly improving the accuracy and robustness of the oil gas concentration detection of the gas station oil tank area environment.

The invention discloses a livestock and poultry house environment big data detection system, which comprises a livestock and poultry house environment parameter acquisition and control platform and a livestock and poultry house environment big data processing subsystem, and is used for detecting and adjusting livestock and poultry environment parameters and predicting the yield. The system effectively solves the problems that an existing livestock and poultry breeding environment has no influence on the yield of the livestock and poultry breeding environment according to nonlinearity and large lag of livestock and poultry breeding environment parameter changes, the livestock and poultry breeding environment is large and complex in area, the yield of the livestock and poultry breeding environment is not predicted, and livestock and poultry breeding environment parameters are not accurately detected and adjusted; therefore, yield prediction and production management of the livestock and poultry breeding environment are greatly influenced.

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 an eggplant greenhouse environment intelligent detection system based on wavelet neural network, which is characterized in that: the detection system is composed of two parts: an eggplant greenhouse environment parameter collection platform based on a wireless sensor network and an eggplant greenhouse output intelligent early warning system; The present invention effectively solves the problem that the output of the existing eggplant greenhouse is not affected by the non-linearity, large lag, large area of ​​the eggplant greenhouse, and complex temperature changes of the eggplant greenhouse. The precise detection and adjustment of temperature will greatly affect the environmental yield prediction and production management of eggplant greenhouses.

The invention discloses an intelligent detection device for eggplant greenhouse temperature based on DRNN neural network, which is characterized in that: the detection device is composed of two parts: an eggplant greenhouse environment parameter acquisition platform based on CAN bus and an eggplant greenhouse temperature intelligent detection system; The invention effectively solves the problem that the existing eggplant greenhouse monitoring system does not detect the temperature of the eggplant greenhouse environment according to the characteristics of nonlinearity, large lag, and complex temperature changes of the eggplant greenhouse environment, which greatly affects the eggplant greenhouse environment. Regulation of ambient temperature.

The invention discloses an intelligent detection system for building energy consumption, which is characterized in that: the system is composed of two parts: a building energy consumption parameter collection platform based on a wireless sensor network and a building energy consumption level classification system, and is based on a wireless sensor network The building energy consumption parameter acquisition platform realizes the detection and monitoring of the environmental parameters affecting the building. The building energy consumption level classification system consists of a temperature detection module, an illuminance detection module, a humidity detection module, an energy consumption prediction module and an interval number kohonen neural network building Composition of material energy consumption state classifiers; the present invention effectively solves the characteristics of existing building energy consumption that is not changed according to building environmental factors, such as nonlinearity, large lag, and complex changes affecting building energy consumption, and intelligently monitors building energy consumption. This will greatly affect the accuracy of building detection.

The invention discloses a multi-point fire early warning system based on the Internet of Things. The system is composed of a ZigBee network-based gas station oil tank area environmental parameter acquisition platform and a gas station oil tank area environment multi-point fire early warning subsystem; The environmental multi-point fire early warning subsystem of the oil tank area of ​​the gas station is composed of multiple parameter detection units and multiple HRFNN fuzzy recursive neural network fire early warning classifiers; The environmental parameters of the oil tank area of ​​the gas station are characterized by nonlinearity, large lag, and complex changes in the environmental parameters of the oil tank area of ​​the gas station. The environmental parameters of the oil tank area of ​​the gas station are accurately detected and the fire warning is carried out, so as to improve the prediction of the fire parameters of the gas station and Fire accuracy issues.

The invention relates to the field of automatic production, and discloses a shading control system, which is characterized in that an NARX neural network model 1, an NARX neural network model 2 and an NARX neural network model 3 are utilized to predict the displacement error, the control quantity and the actual displacement value of a lifting frame respectively, and a dynamic recursive network of the models is established by introducing a delay module and outputting feedback through an NARX neural network; input and output vector delay feedback is introduced into network training, a new input vector is formed, good non-linear mapping ability is achieved, input data including original lifting frame displacement errors, control quantity and actual displacement values and corresponding output data after training are input, the generalization ability of the network is improved. Compared with a traditional static neural network, the single-chip microcomputer controller has better prediction precision and adaptive capacity in the prediction of the corresponding parameters of the lifting frame, and the precision, robustness and reliability of the control system are improved through the single-chip microcomputer controller.

The invention discloses a foundation pit big data detection system which is composed of a foundation pit parameter acquisition platform and a foundation pit parameter big data processing subsystem and is used for realizing foundation pit parameter detection and foundation pit safety prediction and improving the safety and reliability management level of constructional engineering. The method effectively solves the problems that an existing foundation pit has no influence on foundation pit safety according to nonlinearity and large lag of foundation pit parameter change, large foundation pit area, complex parameter change and the like, and the foundation pit parameters are not predicted and the foundation pit safety is not pre-warned, so that the safety management of the foundation pit is greatly influenced.

The invention discloses a fish pond water quality intelligent monitoring system, which is characterized in that: the fish pond water quality parameter detection platform based on a wireless sensor network and a fish pond output intelligent prediction system are composed of two parts; the invention effectively solves the problem of the prior art Only rely on equipment to monitor fish pond water quality parameters, only obtain fish pond water quality parameters, but there is no problem of effectively predicting fish pond production according to the impact of fish pond water quality on fish pond production.

The invention discloses a large data detection system for foundation pits. The system is composed of two parts: a foundation pit parameter acquisition platform and a foundation pit parameter big data processing subsystem. Engineering safety and reliability management level; the present invention effectively solves the influence of the existing foundation pit on the safety of the foundation pit due to the non-linearity, large lag and complex change of the parameters of the large foundation pit area without changing the parameters of the foundation pit. Prediction and early warning of foundation pit safety will greatly affect the safety management of foundation pits.

The invention discloses a multi-point temperature sensor intelligent monitoring system based on a field bus network. The system is composed of an apple orchard environmental parameter acquisition platform and an apple orchard environmental temperature evaluation subsystem. The system realizes intelligent detection and monitoring of the apple orchard environmental temperature. Evaluate the temperature; the present invention effectively solves the problem that the existing apple orchard environmental monitoring is not based on the characteristics of non-linearity, large lag, and large apple orchard area and complex temperature changes of the apple orchard environmental temperature change, and intelligently monitors the temperature of the apple orchard environment The monitoring problem with forecasting, which greatly affects the ambient temperature of apple orchards.

The invention relates to the field of automatic production, and discloses a folding platform control system, which is characterized in that an NARX neural network model 1, an NARX neural network model 2 and an NARX neural network model 3 are utilized to predict the displacement error, the control quantity and the actual displacement value of a lifting platform respectively, and input and output vector delay feedback is introduced into network training to form a new input vector; the system has good non-linear mapping capability, the input of the NARX neural network not only comprises input data of original lifting platform displacement error, control quantity and actual displacement value, but also comprises corresponding output data after training, the generalization capability of the network is improved, and compared with a traditional static neural network, the single-chip microcomputer controller has better prediction precision and adaptive capacity in prediction of corresponding parameters of the lifting platform, and the precision, robustness and reliability of the control system are improved through the single-chip microcomputer controller.

The invention discloses an intelligent monitoring system of multiple oil and gas concentration sensors based on the Internet of Things. The system is composed of a ZigBee network-based gas station oil tank area environmental parameter acquisition platform and a gas station oil tank area environmental oil gas concentration sensor monitoring subsystem. The present invention effectively solves the problem that the existing gas station oil tank area environmental monitoring system does not accurately monitor the gas station oil tank area environmental oil gas concentration according to the characteristics of non-linearity, large hysteresis and complex changes in the gas station oil tank area environmental oil and gas concentration. Detection and early warning of sensor failures, thereby improving the accuracy and robustness of predicting oil and gas concentrations in gas stations.

The invention discloses an intelligent detection system for dust concentration, which is composed of a dust concentration concentration environment parameter collection platform based on CAN bus and an intelligent dust concentration prediction module; the dust concentration concentration environment parameter collection platform based on CAN bus realizes dust detection Concentration environmental factor parameters are detected and adjusted, and the dust concentration intelligent prediction module is composed of a dust concentration interval number neural network model, an interval number prediction model and an interval number CMAC cerebellar neural network dust concentration fusion model; the invention effectively solves the problem of existing industrial and agricultural problems. The production environment does not have the characteristics of non-linearity, large lag and complex dynamic changes according to the change of dust concentration, so it is impossible to accurately detect and predict the dust concentration, which greatly affects the effective management of dust concentration in the industrial and agricultural production environment.

The invention discloses an intelligent oil and gas concentration monitoring system based on a field bus network. The system is composed of a CAN field bus network-based environment parameter acquisition platform for oil tank areas of gas stations and a multi-point oil and gas concentration leakage classifier for the environment of oil tank areas of gas stations. System composition, the system realizes the intelligent detection of the oil and gas leakage concentration in the oil tank area of ​​the gas station and the classification of the oil and gas leakage concentration level; the invention not only effectively solves the problem of the oil and gas concentration detection equipment design of the oil and gas concentration detection device in the oil tank area of ​​the traditional gas station Unreasonable, outdated equipment, imperfect detection system and other reasons lead to many problems in the detection of oil and gas concentration in the oil tank area of ​​the gas station, and effectively solve the existing environmental monitoring system of the oil tank area of ​​the gas station. The concentration of the oil and gas is detected and classified, thereby greatly improving the accuracy and robustness of the detection of the concentration of oil and gas in the tank area of ​​the gas station.

The invention relates to the field of automatic production, and discloses an object transfer cam control system, and the system employs an NARX neural network model 1 and an NARX neural network model 2 to carry out the compensation and the prediction control of the control quantity of a triangular plate, and the NARX neural network builds a dynamic recursive network of a model through the introduction of a time delay module and the output feedback. According to the method, input and output vector delay feedback is introduced into network training to form a new input vector, the system has the good nonlinear mapping capability, the input of the NARX neural network not only comprises original control quantity input data, but also comprises corresponding output data after training, and the generalization capability of the network is improved,. Compared with a traditional static neural network, the object transfer cam control system has better prediction precision and adaptive capacity in triangular disc rotation angle control, and the single-chip microcomputer controller improves the precision, robustness and reliability of a control system.

The invention discloses an intelligent prediction system for orchard output based on the Internet of Things. The system is composed of an apple orchard environmental parameter collection platform and an apple orchard environmental output prediction subsystem. The system realizes the detection of the apple orchard environmental microclimate parameters and the output. Prediction: The present invention effectively solves the problem that the existing apple orchard environmental monitoring system does not accurately detect the environmental temperature of the apple orchard and predict the output according to the characteristics of nonlinearity, large lag and complex changes in the environmental temperature of the apple orchard, thereby improving the forecast of apple orchards. Accuracy and robustness of garden ambient temperature and yield.