44results about How to "Increased ability to process dynamic information" 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 an intelligent granary environment safety monitoring system based on a field bus. The system is composed of a granary environment parameter collection platform based on a CAN field bus, and a granary environment safety evaluation subsystem. The system realizes the intelligent detection of granary environment parameters and the intelligent evaluation of granary environment safety. Many problems still existing in the granary environment caused by the unreasonably designed and poor traditional granary environment multi-parameter detection equipment, the incomplete detection system and the like are solved. Based on the nonlinearity and large lag of granary environment parameter changes and the large area and complex structure of the granary environment, the defects of inaccuracy, low reliability and the like of a granary environment monitoring system are overcome, accurate detection and reliable classification of granary environment parameters are realized, and therefore, the accuracy and robustness of granary environment parameter detection are greatly improved.

The invention discloses a greenhouse environment multi-parameter intelligent monitoring system based on the Internet of Things. The system is composed of a watermelon greenhouse environment parameteracquisition platform based on a ZigBee network and a watermelon greenhouse environment microclimate factor evaluation subsystem. The system realizes intelligent detection of watermelon greenhouse environment temperature and evaluation of microclimate environment factors. The problems that an existing watermelon greenhouse environment monitoring system does not accurately detect watermelon greenhouse environment parameters and evaluate environment factors according to the characteristics of nonlinearity, large lag, complex greenhouse environment parameter changes and the like of greenhouse environment parameter changes, and therefore the accuracy of predicting and evaluating the watermelon greenhouse parameters is improved are effectively solved.

The invention discloses a short-term load predicting method for an Elman neural network based on an improved ABC algorithm. The short-term load predicting method comprises the following steps: takinga series of improving measures specific to defects such as low converging speed of an artificial bee colony (ABC) algorithm and poor developing performance of a searching equation after forward transmission of an input signal of the conventional Elman neural network, backward transmission of an error signal and a delay operator of a carrying layer are fully analyzed, wherein the improving measuresinclude re-designing a searching equation, adjusting the honey searching frequency and changing the selection mechanism of an optimal solution and the like; applying an optimal weight generated by the improved ABC algorithm and a threshold value to the Elman neural network to realize short-term load prediction on a power system, and increasing the load prediction speed; and lastly, implementing aload prediction function in MATLAB, and optimizing the weight and the threshold value by adopting the improved ABC algorithm according to an experiment result, so that the maximum prediction absoluteerror is lowered remarkably.

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 tomato greenhouse temperature early-warning system based on a minimum vector machine. The early-warning system is characterized by being composed of a tomato greenhouse environmental parameter acquisition and intelligent prediction platform based on a CAN field bus and an intelligent tomato greenhouse temperature early-warning system. By means of the intelligent tomato greenhouse temperature early-warning system based on the minimum vector machine in the invention, many problems still in the environment in a closed tomato greenhouse due to the reasons ofunreasonable design, backward equipment, incomplete control system and the like in the traditional tomato greenhouse environment can be effectively solved; and furthermore, the control problem that the tomato greenhouse environment temperature is greatly influenced due to the fact that the existing tomato greenhouse environment monitoring system does not monitor and predict the temperature in thetomato greenhouse environment according to the characteristics of nonlinearity and large lag of tomato greenhouse environmental temperature change, large tomato greenhouse area, complex temperature change and the like can be effectively solved.

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 building settlement detection system which is composed of a building settlement parameter acquisition platform based on a wireless sensor network and a buildingsettlement intelligent early warning system, and the building settlement parameter acquisition platform based on the wireless sensor network realizes detection and management of building settlement parameters. The system effectively solves the problems that existing building settlement has no influence on the settlement of the whole building according to nonlinearity, large lag, complex settlementchange and the like of settlement amount change of each detection point of the building; accurate detection, prediction and early warning are not carried out on building settlement, so that early warning and management of the building settlement amount are greatly influenced.

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 a food safety detection system, which is composed of a food environment parameter acquisition platform and a food freshness big data processing subsystem.According to the influence of the storage, transportation and fresh-keeping environment factor parameters of the food and the fresh-keeping time on the freshness of the food, environmental factor parameters and time of the food in different links are collected to identify the freshness of the food, and beneficial reference is provided for environmental factor adjustment and corresponding time determination of food storage, transportation and fresh keeping; the food safety detection system effectively solves the problem that the food quality is greatly influenced due to the nonlinearity and large lag of food processing and transportation environment temperature parameter change in different circulation stages in the prior art.

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 an intelligent building safety detection system. The intelligent building safety detection system is characterized by being composed of a building safety parameter acquisitionplatform based on a CAN field bus and a building safety measurement system. Many problems of untimely, inaccurate and unreliable building safety detection and the like caused by unreasonable design, backward equipment, imperfect control system and the like of the traditional building safety detection system are effectively solved, and moreover, the problems that an existing building safety detection system does not have the characteristics of nonlinearity, large lag, complex building deformation change and the like of building settlement, heeling and translation change, settlement, heeling angle and translation influencing building safety are monitored and predicted, and thus regulation and control of building deformation are greatly influenced are effectively solved.

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 greenhouse environment multi-parameter intelligent monitoring system based on the Internet of Things. The system is composed of a ZigBee network-based watermelon greenhouse environment parameter collection platform and a watermelon greenhouse environment microclimate factor evaluation subsystem. The system realizes watermelon greenhouse environment monitoring. The intelligent detection of ambient temperature and the evaluation of microclimate environmental factors; the present invention effectively solves the characteristics that the existing watermelon greenhouse environment monitoring system does not change according to the greenhouse environment parameters, such as nonlinearity, large lag, and complex changes in greenhouse environment parameters. Parameters are accurately detected and environmental factors are evaluated, so as to improve the accuracy of prediction and evaluation of watermelon greenhouse parameters.

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.