CN118224112A - Intelligent electric fan control system and method based on Internet of things technology - Google Patents
Intelligent electric fan control system and method based on Internet of things technology Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D27/00—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
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- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y20/00—Information sensed or collected by the things
- G16Y20/10—Information sensed or collected by the things relating to the environment, e.g. temperature; relating to location
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/30—Control
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2270/00—Control
- F05D2270/70—Type of control algorithm
- F05D2270/71—Type of control algorithm synthesized, i.e. parameter computed by a mathematical model
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Abstract
The application discloses an intelligent electric fan control system and method based on the internet of things technology, which acquire a time sequence of ambient temperature and a time sequence of wind speed; data normalization is carried out on the time sequence of the ambient temperature and the time sequence of the wind speed so as to obtain a sequence of the local time sequence input vector of the ambient temperature and a sequence of the local time sequence input vector of the wind speed; calculating a sample covariance correlation matrix between each group of corresponding ambient temperature local time sequence input vectors and wind speed local time sequence input vectors in the sequence of the ambient temperature local time sequence input vectors and the sequence of the wind speed local time sequence input vectors to obtain a sequence of temperature-wind speed correlation matrices in a local time domain; based on implicit correlation characteristics of the sequence of local time domain temperature-wind speed correlation matrices, fan control instructions are determined. Therefore, the power of the electric fan can be automatically adjusted according to the change of the ambient temperature and the wind speed, so that the electric fan can provide proper wind speed under different ambient conditions, and the comfort level of a user is improved.
Description
Technical Field
The application relates to the technical field of intelligent electric fan control, in particular to an intelligent electric fan control system and method based on the internet of things technology.
Background
The electric fan is a common living article, and can reduce the body temperature and the sense temperature of a human body through blowing, so that the comfort level of people is improved. However, the conventional electric fan generally requires a user to manually adjust parameters such as a switch, a wind speed, etc., which not only brings inconvenience to the user but also causes waste of energy. For example, when the ambient temperature decreases, the electric fan may still be operated at a higher power, resulting in a loss of electrical energy. Accordingly, an intelligent electric fan control system and method are desired.
Disclosure of Invention
The application provides an intelligent electric fan control system and method based on the internet of things technology, which acquire a time sequence of ambient temperature and a time sequence of wind speed; data normalization is carried out on the time sequence of the ambient temperature and the time sequence of the wind speed so as to obtain a sequence of the local time sequence input vector of the ambient temperature and a sequence of the local time sequence input vector of the wind speed; calculating a sample covariance correlation matrix between each group of corresponding ambient temperature local time sequence input vectors and wind speed local time sequence input vectors in the sequence of the ambient temperature local time sequence input vectors and the sequence of the wind speed local time sequence input vectors to obtain a sequence of temperature-wind speed correlation matrices in a local time domain; based on implicit correlation characteristics of the sequence of local time domain temperature-wind speed correlation matrices, fan control instructions are determined. Therefore, the power of the electric fan can be automatically adjusted according to the change of the ambient temperature and the wind speed, so that the electric fan can provide proper wind speed under different ambient conditions, and the comfort level of a user is improved.
The application also provides an intelligent electric fan control method based on the internet of things technology, which comprises the following steps:
Acquiring a time sequence of ambient temperatures acquired by a temperature sensor;
Acquiring a time sequence of wind speeds acquired by a wind speed sensor;
Data normalization is carried out on the time sequence of the ambient temperature and the time sequence of the wind speed to obtain a sequence of the local time sequence input vector of the ambient temperature and a sequence of the local time sequence input vector of the wind speed;
Calculating a sample covariance correlation matrix between each group of corresponding local time sequence input vectors of the environment temperature and the local time sequence input vectors of the wind speed in the sequence of the local time sequence input vectors of the environment temperature and the local time sequence input vectors of the wind speed to obtain a sequence of temperature-wind speed correlation matrices in a local time domain;
determining a fan control instruction based on implicit correlation characteristics of the sequence of local time domain temperature-wind speed correlation matrices.
In the above intelligent electric fan control method based on the internet of things, the data normalization is performed on the time sequence of the ambient temperature and the time sequence of the wind speed to obtain a sequence of local time sequence input vectors of the ambient temperature and a sequence of local time sequence input vectors of the wind speed, including: performing sequence segmentation on the time sequence of the ambient temperature and the time sequence of the wind speed based on a preset time scale to obtain a sequence of a local time sequence of the ambient temperature and a sequence of a local time sequence of the wind speed; and respectively normalizing the sequence of the local time sequence of the ambient temperature and the sequence of the local time sequence of the wind speed according to a time dimension to obtain the sequence of the local time sequence input vector of the ambient temperature and the sequence of the local time sequence input vector of the wind speed.
In the above intelligent electric fan control method based on the internet of things, calculating a sample covariance correlation matrix between each group of corresponding local time sequence input vectors of the environment temperature and the local time sequence input vectors of the wind speed in the sequence of the local time sequence input vectors of the environment temperature and the local time sequence input vectors of the wind speed to obtain a sequence of a temperature-wind speed correlation matrix in a local time domain, including: calculating a sample covariance matrix of the ambient temperature local time sequence input vector relative to the wind speed local time sequence input vector according to the following sample covariance correlation formula to obtain a temperature-wind speed correlation matrix in the local time domain; the sample covariance correlation formula is as follows: ; wherein/> Inputting a vector for the local timing of the ambient temperature,/>Inputting a vector for the local time sequence of wind speeds,/>And (5) the local time domain temperature-wind speed correlation matrix.
In the intelligent electric fan control method based on the internet of things, determining a fan control instruction based on implicit correlation characteristics of a sequence of the local time domain temperature-wind speed correlation matrix includes: performing implicit feature extraction on each local time domain temperature-wind speed correlation matrix in the sequence of the local time domain temperature-wind speed correlation matrix by using a deep learning network model to obtain a sequence of local time domain temperature-wind speed correlation feature vectors; the sequence of the temperature-wind speed associated characteristic vectors in the local time domain passes through an importance distribution network based on the self-adaptive attention module to obtain a weighted sequence of the temperature-wind speed associated characteristic vectors in the local time domain; carrying out fusion optimization treatment on the sequence of the temperature-wind speed associated characteristic vectors in the local time domain and the sequence of the weighted local time domain to obtain an optimized full-time domain temperature-wind speed associated characteristic vector; and the optimized full-time domain temperature-wind speed correlation characteristic vector is passed through a classifier to obtain the fan control instruction, wherein the fan control instruction is used for indicating whether the electric fan power at the current time point can be reduced.
In the intelligent electric fan control method based on the internet of things, the deep learning network model is a temperature-wind speed correlation feature extractor based on a convolutional neural network model.
In the intelligent electric fan control method based on the internet of things, the implicit feature extraction is performed on each local time domain temperature-wind speed correlation matrix in the sequence of the local time domain temperature-wind speed correlation matrix by using a deep learning network model to obtain a sequence of local time domain temperature-wind speed correlation feature vectors, including: and passing each local time domain temperature-wind speed correlation matrix in the sequence of local time domain temperature-wind speed correlation matrices through the convolutional neural network model-based temperature-wind speed correlation feature extractor to obtain the sequence of local time domain temperature-wind speed correlation feature vectors.
In the above intelligent electric fan control method based on the internet of things, the step of obtaining the weighted sequence of the temperature-wind speed associated feature vectors in the local time domain by using the importance distribution network based on the adaptive attention module includes: processing the sequence of the local time domain temperature-wind speed associated feature vectors with the following importance allocation formula to obtain the sequence of weighted local time domain temperature-wind speed associated feature vectors; wherein, the importance allocation formula is: ; wherein/> For the/>, in the sequence of temperature-wind speed associated feature vectors in the local time domainA local time domain temperature-wind speed correlation eigenvector,For pooling processing,/>For pooling vectors,/>Is a weight matrix,/>Is a bias vector,/>In order to activate the process,For the initial meta-weight feature vector,/>Is the/>, of the initial meta-weight feature vectorCharacteristic value of individual position,/>To correct the meta-weight feature vector,/>Is the/>, of the correction element weight feature vectorCharacteristic value of individual position,/>Is the/>, in the sequence of the weighted local time domain temperature-wind speed correlation eigenvectorsTemperature-wind speed associated characteristic vector in local time domain after weighting,/>The dot product process is represented.
In the above intelligent electric fan control method based on the internet of things, the obtaining the fan control instruction by passing the optimized full-time domain temperature-wind speed correlation feature vector through a classifier, where the fan control instruction is used to indicate whether the electric fan power at the current time point can be reduced, includes: performing full-connection coding on the optimized full-time domain temperature-wind speed associated feature vector by using a plurality of full-connection layers of the classifier to obtain a coded classification feature vector; and passing the coding classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
The application also provides an intelligent electric fan control system based on the internet of things technology, which comprises:
The time sequence acquisition module of the ambient temperature is used for acquiring the time sequence of the ambient temperature acquired by the temperature sensor;
The time sequence acquisition module of the wind speed is used for acquiring the time sequence of the wind speed acquired by the wind speed sensor;
the data normalization module is used for performing data normalization on the time sequence of the ambient temperature and the time sequence of the wind speed to obtain a sequence of the local time sequence input vector of the ambient temperature and a sequence of the local time sequence input vector of the wind speed;
The sample covariance incidence matrix calculation module is used for calculating a sample covariance incidence matrix between each group of corresponding local time sequence input vectors of the environment temperature and the wind speed in the sequence of the local time sequence input vectors of the environment temperature and the wind speed so as to obtain a sequence of temperature-wind speed incidence matrices in a local time domain;
And the fan control instruction determining module is used for determining a fan control instruction based on implicit correlation characteristics of the sequence of the temperature-wind speed correlation matrix in the local time domain.
In the intelligent electric fan control system based on the internet of things, the data normalization module comprises: the sequence segmentation unit is used for carrying out sequence segmentation on the time sequence of the ambient temperature and the time sequence of the wind speed based on a preset time scale so as to obtain a sequence of a local time sequence of the ambient temperature and a sequence of a local time sequence of the wind speed; and the sequence normalization unit is used for normalizing the sequence of the local time sequence of the ambient temperature and the sequence of the local time sequence of the wind speed according to the time dimension to obtain the sequence of the local time sequence input vector of the ambient temperature and the sequence of the local time sequence input vector of the wind speed.
Compared with the prior art, the intelligent electric fan control system and the method based on the Internet of things technology acquire the time sequence of the ambient temperature and the time sequence of the wind speed; data normalization is carried out on the time sequence of the ambient temperature and the time sequence of the wind speed so as to obtain a sequence of the local time sequence input vector of the ambient temperature and a sequence of the local time sequence input vector of the wind speed; calculating a sample covariance correlation matrix between each group of corresponding ambient temperature local time sequence input vectors and wind speed local time sequence input vectors in the sequence of the ambient temperature local time sequence input vectors and the sequence of the wind speed local time sequence input vectors to obtain a sequence of temperature-wind speed correlation matrices in a local time domain; based on implicit correlation characteristics of the sequence of local time domain temperature-wind speed correlation matrices, fan control instructions are determined. Therefore, the power of the electric fan can be automatically adjusted according to the change of the ambient temperature and the wind speed, so that the electric fan can provide proper wind speed under different ambient conditions, and the comfort level of a user is improved.
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In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
Fig. 1 is a flowchart of an intelligent electric fan control method based on the internet of things technology provided in an embodiment of the present application.
Fig. 2 is a schematic diagram of a system architecture of an intelligent electric fan control method based on the internet of things technology according to an embodiment of the present application.
Fig. 3 is a block diagram of an intelligent electric fan control system based on the internet of things technology according to an embodiment of the present application.
Fig. 4 is an application scenario diagram of an intelligent electric fan control method based on the internet of things technology provided in an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the embodiments of the present application will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present application and their descriptions herein are for the purpose of explaining the present application, but are not to be construed as limiting the application.
Unless defined otherwise, all technical and scientific terms used in the embodiments of the application have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present application.
In describing embodiments of the present application, unless otherwise indicated and limited thereto, the term "connected" should be construed broadly, for example, it may be an electrical connection, or may be a communication between two elements, or may be a direct connection, or may be an indirect connection via an intermediate medium, and it will be understood by those skilled in the art that the specific meaning of the term may be interpreted according to circumstances.
It should be noted that, the term "first\second\third" related to the embodiment of the present application is merely to distinguish similar objects, and does not represent a specific order for the objects, it is to be understood that "first\second\third" may interchange a specific order or sequence where allowed. It is to be understood that the "first\second\third" distinguishing objects may be interchanged where appropriate such that embodiments of the application described herein may be practiced in sequences other than those illustrated or described herein.
An electric fan is a common household appliance generally used to provide air flow and cool down in an indoor space, and air flow is generated by rotating blades, thereby circulating indoor air, reducing indoor temperature, improving air quality, and improving comfort. The electric fan is typically driven by an electric motor, which rotates blades via a shaft connected to the blades, the rotation of the blades causing a flow of ambient air, thereby creating wind. Electric fans typically have different speed settings and the wind speed can be adjusted as desired.
Besides cooling, the electric fans can provide a certain degree of comfort when the air conditioner is not available or unnecessary, and some electric fans also have a head shaking function and can swing left and right or up and down to cover a wider area. In some areas, people prefer to use electric fans rather than air conditioners due to energy saving and environmental concerns. Electric fans are generally more energy efficient than air conditioning and may provide adequate comfort in some situations where the weather is not very hot.
Conventional electric fan control systems have some drawbacks, and conventional electric fans often require a user to manually adjust parameters such as a switch, a wind speed, etc., which causes inconvenience to the user, especially in cases where frequent adjustment of wind speed or switch is required. Because traditional electric fan lacks intelligent control, when ambient temperature reduces, the electric fan probably still runs with higher power, leads to the waste of electric energy, and under this kind of circumstances, the electric fan can not carry out intelligent regulation according to actual need, has caused the waste of energy. Conventional electric fans often lack automation functionality and cannot automatically adjust wind speed and switching according to ambient temperature, humidity or user requirements, which makes them limited in energy efficiency and user experience.
In order to solve these problems, the intelligent electric fan control system and method should be capable of realizing automatic adjustment, intelligently adjusting wind speed and switching according to environmental conditions and user demands to improve energy utilization efficiency and user experience, which may include using sensors to monitor environmental conditions, automatically adjusting the running state of the fan by adopting an intelligent algorithm, and even integrating with an intelligent home system to realize remote control and intelligent management.
In one embodiment of the present application, fig. 1 is a flowchart of an intelligent electric fan control method based on the internet of things technology provided in the embodiment of the present application. Fig. 2 is a schematic diagram of a system architecture of an intelligent electric fan control method based on the internet of things technology according to an embodiment of the present application. As shown in fig. 1 and 2, the intelligent electric fan control method based on the internet of things technology according to the embodiment of the application includes: 110, acquiring a time sequence of the ambient temperature acquired by the temperature sensor; 120, acquiring a time sequence of wind speeds acquired by a wind speed sensor; 130, performing data normalization on the time sequence of the ambient temperature and the time sequence of the wind speed to obtain a sequence of local time sequence input vectors of the ambient temperature and a sequence of local time sequence input vectors of the wind speed; 140, calculating a sample covariance correlation matrix between each group of corresponding local time sequence input vectors of the environment temperature and the local time sequence input vectors of the wind speed in the sequence of the local time sequence input vectors of the environment temperature and the local time sequence input vectors of the wind speed to obtain a sequence of a temperature-wind speed correlation matrix in a local time domain; 150, determining a fan control instruction based on implicit correlation characteristics of the sequence of local time domain temperature-wind speed correlation matrices.
In the step 110, a time sequence of the ambient temperature collected by the temperature sensor is obtained, accuracy and stability of the temperature sensor are ensured, so as to obtain reliable ambient temperature data, and basic data is provided for subsequent steps by obtaining an accurate time sequence of the ambient temperature. In the step 120, a time series of wind speeds acquired by a wind speed sensor is acquired, accuracy and stability of the wind speed sensor are ensured, so as to acquire reliable wind speed data, and basic data is provided for subsequent steps by acquiring an accurate time series of wind speeds. In the step 130, the time sequence of the ambient temperature and the time sequence of the wind speed are subjected to data normalization to obtain a sequence of the local time sequence input vector of the ambient temperature and a sequence of the local time sequence input vector of the wind speed, so as to ensure that the proper time sequence normalization, such as time alignment, uniform sampling rate and the like, is performed on the ambient temperature and the wind speed data, and preparation is provided for the data processing and analysis of the subsequent steps by obtaining the normalized sequence of the local time sequence input vector of the ambient temperature and the wind speed. In the step 140, a sequence of the local time sequence input vectors of the ambient temperature and a sample covariance correlation matrix between each group of the corresponding local time sequence input vectors of the wind speed and the corresponding local time sequence input vectors of the wind speed are calculated to obtain a sequence of a temperature-wind speed correlation matrix in a local time domain, so that a time relation among samples is considered in the calculation process, an accurate local time domain temperature-wind speed correlation matrix is obtained, and the correlation matrix sequence between the ambient temperature and the wind speed is obtained for subsequent analysis of correlation characteristics between the temperature and the wind speed. In the step 150, based on the implicit correlation characteristics of the sequence of the temperature-wind speed correlation matrix in the local time domain, a fan control instruction is determined, effective feature extraction and analysis are performed by using the implicit characteristics of the correlation matrix to determine an appropriate fan control instruction, and the fan control instruction is intelligently determined according to the correlation characteristics between the ambient temperature and the wind speed, so that intelligent fan control is realized, and the energy utilization efficiency and the user experience are improved.
The traditional electric fan generally needs to manually adjust parameters such as a switch, wind speed and the like, which not only brings inconvenience to users, but also causes energy waste. For example, when the ambient temperature decreases, the electric fan may still be operated at a higher power, resulting in a loss of electrical energy. In order to implement an intelligent electric fan control system, a variety of factors need to be considered, of which the most important are ambient temperature and wind speed. Ambient temperature is one of the important factors affecting the human sense temperature, and wind speed is one of the important factors affecting the power and effect of the electric fan. Therefore, if the association relation between the ambient temperature and the wind speed can be established, and a proper fan control instruction is generated according to the association relation, the intelligent electric fan control system can be realized.
However, the correlation between ambient temperature and wind speed is not a simple linear relationship, but a complex time-varying dynamic relationship. In this regard, the technical concept of the present application is as follows: acquiring a time sequence of the ambient temperature acquired by a temperature sensor and a time sequence of the wind speed acquired by a wind speed sensor, extracting an implicit association relation between the ambient temperature and the wind speed by using a deep learning model, and realizing intelligent regulation of the power of the electric fan according to the implicit association relation.
The deep learning model has strong nonlinear modeling capability, and can learn and capture complex nonlinear relations. The correlation between ambient temperature and wind speed tends to be nonlinear, and conventional linear models or simple machine learning algorithms may not accurately model such complex relationships. The deep learning model can learn a more complex nonlinear mapping relation through the combination of a plurality of hidden layers and an activation function, so that the hidden association relation between the ambient temperature and the wind speed is better captured. Therefore, the power of the electric fan is automatically adjusted according to the change of the ambient temperature and the wind speed, so that the electric fan provides proper wind speed under different ambient conditions, and the comfort level of a user is improved.
Based on this, in the technical solution of the present application, firstly, a time series of the ambient temperature acquired by the temperature sensor is acquired; and a time series of wind speeds acquired by the wind speed sensor is acquired. It should be appreciated that changes in ambient temperature have a significant impact on the comfort of the human body. In hot weather, lower ambient temperatures may be more comfortable, while in cold weather, higher ambient temperatures are more comfortable. Thus, acquiring a time series of ambient temperatures may help to analyze real-time environmental conditions. In addition, wind speed is also an important factor affecting human body's perceived temperature. Different ambient conditions may require different wind speeds to achieve a comfortable effect. Therefore, the time sequence of the environmental temperature and the time sequence of the wind speed can be obtained to monitor the environmental change in real time, and the power of the electric fan can be automatically regulated according to the actual situation, so that more comfortable and personalized wind speed experience is provided, and the waste of energy sources is reduced.
Then, performing sequence segmentation on the time sequence of the ambient temperature and the time sequence of the wind speed based on a preset time scale to obtain a sequence of a local time sequence of the ambient temperature and a sequence of a local time sequence of the wind speed; and the sequence of the local time sequence of the ambient temperature and the sequence of the local time sequence of the wind speed are respectively regulated according to the time dimension to obtain the sequence of the local time sequence input vector of the ambient temperature and the sequence of the local time sequence input vector of the wind speed. The time series of the ambient temperature and the time series of the wind speed often contain a large amount of information, and if the time series of the ambient temperature and the time series of the wind speed are directly subjected to data analysis and feature extraction, the computational complexity may be increased. Here, by performing a sequence slicing on the time series of the ambient temperature and the time series of the wind speed based on a predetermined time scale, the entire time series can be sliced into smaller partial sequences, thereby guiding the model to better capture the partial changes of the ambient temperature and the wind speed while reducing the calculation amount of the subsequent model. And then the sequence of the local time sequence of the ambient temperature and the sequence of the local time sequence of the wind speed are respectively regulated according to the time dimension, so that the data structure can be integrated into a unified vector representation, and a more convenient and reliable data form is provided for further data analysis and processing.
In a specific embodiment of the present application, the data normalization is performed on the time series of the ambient temperature and the time series of the wind speed to obtain a sequence of local time series input vectors of the ambient temperature and a sequence of local time series input vectors of the wind speed, including: performing sequence segmentation on the time sequence of the ambient temperature and the time sequence of the wind speed based on a preset time scale to obtain a sequence of a local time sequence of the ambient temperature and a sequence of a local time sequence of the wind speed; and respectively normalizing the sequence of the local time sequence of the ambient temperature and the sequence of the local time sequence of the wind speed according to a time dimension to obtain the sequence of the local time sequence input vector of the ambient temperature and the sequence of the local time sequence input vector of the wind speed.
And then, calculating a sample covariance correlation matrix between each group of corresponding ambient temperature local time sequence input vectors and wind speed local time sequence input vectors in the sequence of the ambient temperature local time sequence input vectors and the sequence of the wind speed local time sequence input vectors to obtain a sequence of temperature-wind speed correlation matrices in a local time domain. That is, the correlation between each set of corresponding ambient temperature local time series input vectors and wind speed local time series input vectors is constructed by calculating a sample covariance correlation matrix. Specifically, the sample covariance correlation matrix reflects the strength and direction of the linear relationship between the local time sequence input vector of the ambient temperature and the local time sequence input vector of the wind speed, and the correlation between the ambient temperature and the wind speed can be known by analyzing the elements of the covariance matrix.
In a specific embodiment of the present application, calculating a sample covariance correlation matrix between each set of corresponding ambient temperature local time sequence input vectors and wind speed local time sequence input vectors in the sequence of ambient temperature local time sequence input vectors and the sequence of wind speed local time sequence input vectors to obtain a sequence of temperature-wind speed correlation matrices in a local time domain includes: calculating a sample covariance matrix of the ambient temperature local time sequence input vector relative to the wind speed local time sequence input vector according to the following sample covariance correlation formula to obtain a temperature-wind speed correlation matrix in the local time domain; the sample covariance correlation formula is as follows: ; wherein/> A vector is input for the local timing of the ambient temperature,Inputting a vector for the local time sequence of wind speeds,/>And (5) the local time domain temperature-wind speed correlation matrix.
Further, in one embodiment of the present application, determining the fan control instruction based on implicit correlation characteristics of the sequence of local time domain temperature-wind speed correlation matrices includes: performing implicit feature extraction on each local time domain temperature-wind speed correlation matrix in the sequence of the local time domain temperature-wind speed correlation matrix by using a deep learning network model to obtain a sequence of local time domain temperature-wind speed correlation feature vectors; the sequence of the temperature-wind speed associated characteristic vectors in the local time domain passes through an importance distribution network based on the self-adaptive attention module to obtain a weighted sequence of the temperature-wind speed associated characteristic vectors in the local time domain; carrying out fusion optimization treatment on the sequence of the temperature-wind speed associated characteristic vectors in the local time domain and the sequence of the weighted local time domain to obtain an optimized full-time domain temperature-wind speed associated characteristic vector; and the optimized full-time domain temperature-wind speed correlation characteristic vector is passed through a classifier to obtain the fan control instruction, wherein the fan control instruction is used for indicating whether the electric fan power at the current time point can be reduced.
And then, passing each local time domain temperature-wind speed correlation matrix in the sequence of local time domain temperature-wind speed correlation matrices through a temperature-wind speed correlation feature extractor based on a convolutional neural network model to obtain a sequence of local time domain temperature-wind speed correlation feature vectors. Among other things, convolutional Neural Network (CNN) models are adept at capturing complex local spatial and temporal patterns. By taking the temperature-wind speed correlation matrix in each local time domain as input, the CNN can automatically learn the local mode, the spatial relationship and the time sequence characteristics, so that the implicit correlation characteristics between the temperature and the wind speed in the local neighborhood can be better identified. More specifically, the CNN model may extract local neighborhood-related features of temperature and wind speed by stacking convolution layers and pooling layers. And, the nonlinear nature of the CNN model enables it to model complex temperature-wind speed correlation features. By using an appropriate activation function (e.g., reLU) and a non-linear layer (e.g., batch Normalization), CNN can better accommodate the correlation patterns between various temperatures and wind speeds.
The deep learning network model is a temperature-wind speed correlation feature extractor based on a convolutional neural network model.
Further, in a specific embodiment of the present application, performing implicit feature extraction on each local time domain temperature-wind speed correlation matrix in the sequence of local time domain temperature-wind speed correlation matrices by using a deep learning network model to obtain a sequence of local time domain temperature-wind speed correlation feature vectors, including: and passing each local time domain temperature-wind speed correlation matrix in the sequence of local time domain temperature-wind speed correlation matrices through the convolutional neural network model-based temperature-wind speed correlation feature extractor to obtain the sequence of local time domain temperature-wind speed correlation feature vectors.
Further, the sequence of the temperature-wind speed correlation characteristic vectors in the local time domain passes through an importance distribution network based on the adaptive attention module to obtain the weighted sequence of the temperature-wind speed correlation characteristic vectors in the local time domain. Here, the sequence of the temperature-wind speed associated feature vectors in the local time domain can be dynamically allocated to the temperature-wind speed associated feature vectors in each local time domain based on the importance degree through the importance allocation network based on the adaptive attention module, so that the weighted temperature-wind speed associated feature vectors in the local time domain can highlight important feature areas, and the features related to fan control are focused in a targeted manner.
In a specific embodiment of the present application, passing the sequence of the local time domain temperature-wind speed associated feature vectors through an importance distribution network based on an adaptive attention module to obtain a weighted sequence of local time domain temperature-wind speed associated feature vectors includes: processing the sequence of the local time domain temperature-wind speed associated feature vectors with the following importance allocation formula to obtain the sequence of weighted local time domain temperature-wind speed associated feature vectors; wherein, the importance allocation formula is: ; wherein/> For the/>, in the sequence of temperature-wind speed associated feature vectors in the local time domainTemperature-wind speed correlation characteristic vector in local time domain,/>For the purpose of the pooling treatment,For pooling vectors,/>Is a weight matrix,/>Is a bias vector,/>For the activation process,/>For the initial meta-weight feature vector,/>Is the/>, of the initial meta-weight feature vectorCharacteristic value of individual position,/>To correct the meta-weight feature vector,/>Is the/>, of the correction element weight feature vectorCharacteristic value of individual position,/>Is the/>, in the sequence of the weighted local time domain temperature-wind speed correlation eigenvectorsThe weighted local time domain temperature-wind speed correlation eigenvectors,The dot product process is represented.
In the technical scheme of the application, the sequence of the local time domain temperature-wind speed correlation feature vector expresses the high-order local time sequence correlation feature of the local time domain time sequence full covariance correlation of the ambient temperature and the wind speed in the local time domain determined by sequence segmentation in the global time domain, so that the sequence of the local time domain temperature-wind speed correlation feature vector can strengthen the global distribution of the sequence of the local time domain temperature-wind speed correlation feature vector based on the local time domain after passing through an importance distribution network based on an adaptive attention module, thereby strengthening the expression effect of the sequence of the weighted local time domain temperature-wind speed correlation feature vector.
But this also causes the time-series correlated feature representation of the sequence of weighted local time-domain temperature-wind velocity correlated feature vectors to deviate from the time-series correlated feature representation of the sequence of local time-domain temperature-wind velocity correlated feature vectors based on the local time-domain distribution, and thus the applicant of the present application considers improving the semantic feature representation of the sequence of weighted local time-domain temperature-wind velocity correlated feature vectors by further fusing the sequence of local time-domain temperature-wind velocity correlated feature vectors with the sequence of weighted local time-domain temperature-wind velocity correlated feature vectors.
In addition, in order to promote consistency of the distribution information representation in fusion, the applicant performs fusion optimization on the sequence of the local time domain temperature-wind speed correlation eigenvectors and the sequence of the weighted local time domain temperature-wind speed correlation eigenvectors, specifically expressed as follows: carrying out fusion optimization on the sequence of the temperature-wind speed associated characteristic vectors in the local time domain and the sequence of the weighted local time domain by using the following optimization formula to obtain an optimized full-time domain temperature-wind speed associated characteristic vector; wherein, the optimization formula is: ; wherein/> Is the first eigenvector obtained by cascading the sequence of the temperature-wind speed related eigenvectors in the local time domain, andIs a second eigenvector obtained by cascading the weighted sequence of temperature-wind speed associated eigenvectors in the local time domain,And/>Respectively representing squares of 1-norm and 2-norm of the feature vector, the first feature vector/>And a second eigenvector/>Having the same eigenvector length/>And/>Is a weight superparameter,/>And/>Is the eigenvalue of the first eigenvector and the second eigenvector,/>, respectivelyIs the characteristic value of the optimized full-time domain temperature-wind speed correlation characteristic vector,/>A logarithmic function with a base of 2 is shown.
Here, in order to promote the consistency of the sequence of the temperature-wind speed associated feature vectors in the local time domain and the distribution information representation of the sequence of the temperature-wind speed associated feature vectors in the weighted local time domain in the feature fusion scene, the absolute coordinates of the distribution regression are predefined by the feature scale and the structural representation of the feature vectors to be fused to serve as the reference of the feature value cross geometric registration, so that the consistency of the rigid grid of the information distribution can be maintained, and the misalignment and the incomplete overlapping based on the distance between the feature distribution information representations are punished by the thought of the probability chamfering loss, so that the consistent feature fusion of the sequence of the temperature-wind speed associated feature vectors in the local time domain and the distribution information representation of the sequence of the temperature-wind speed associated feature vectors in the weighted local time domain is realized. Therefore, the expression effect of the sequence of the weighted local time domain temperature-wind speed associated feature vector is improved, the expression effect of the full-time domain temperature-wind speed associated feature vector obtained by cascading the sequence of the weighted local time domain temperature-wind speed associated feature vector is improved, and the accuracy of the classification result obtained by the classifier is improved.
And then, the optimized full-time domain temperature-wind speed correlation characteristic vector is passed through a classifier to obtain a fan control instruction, wherein the fan control instruction is used for indicating whether the electric fan power at the current time point can be reduced. That is, the information of the local time sequence is integrated into the temperature-wind speed correlation characteristic distribution of the whole time domain through cascading so as to comprehensively represent the temperature-wind speed correlation mode and dynamic change in the whole time domain range. And then a classifier is utilized to learn the mapping relation from the optimized full-time domain temperature-wind speed association characteristic vector to a fan control instruction, so that whether the power of the electric fan can be reduced is intelligently judged.
In a specific embodiment of the present application, the optimized full time domain temperature-wind speed correlation feature vector is passed through a classifier to obtain the fan control instruction, where the fan control instruction is used to indicate whether the electric fan power at the current time point can be reduced, and the method includes: performing full-connection coding on the optimized full-time domain temperature-wind speed associated feature vector by using a plurality of full-connection layers of the classifier to obtain a coded classification feature vector; and passing the coding classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
In summary, an intelligent electric fan control method based on the internet of things technology according to an embodiment of the present application is illustrated, which obtains a time sequence of an ambient temperature collected by a temperature sensor and a time sequence of a wind speed collected by a wind speed sensor, extracts an implicit association relation between the ambient temperature and the wind speed by using a deep learning model, and realizes intelligent adjustment of electric fan power according to the implicit association relation.
Fig. 3 is a block diagram of an intelligent electric fan control system based on the internet of things technology according to an embodiment of the present application. As shown in fig. 3, the intelligent electric fan control system 200 based on the internet of things technology includes: a time series acquisition module 210 for acquiring a time series of the ambient temperature acquired by the temperature sensor; a time series acquisition module 220 for acquiring a time series of wind speeds acquired by the wind speed sensor; a data normalization module 230, configured to normalize the time sequence of the ambient temperature and the time sequence of the wind speed to obtain a sequence of an ambient temperature local time sequence input vector and a sequence of a wind speed local time sequence input vector; the sample covariance correlation matrix calculation module 240 is configured to calculate a sample covariance correlation matrix between each set of corresponding local time-sequence input vectors of the ambient temperature and each set of corresponding local time-sequence input vectors of the wind speed in the sequence of local time-sequence input vectors of the ambient temperature and the wind speed so as to obtain a sequence of temperature-wind speed correlation matrices in a local time domain; a fan control instruction determination module 250 is configured to determine a fan control instruction based on implicit correlation characteristics of the sequence of the local time domain temperature-wind speed correlation matrix.
In the intelligent electric fan control system based on the internet of things technology, the data normalization module comprises: the sequence segmentation unit is used for carrying out sequence segmentation on the time sequence of the ambient temperature and the time sequence of the wind speed based on a preset time scale so as to obtain a sequence of a local time sequence of the ambient temperature and a sequence of a local time sequence of the wind speed; and the sequence normalization unit is used for normalizing the sequence of the local time sequence of the ambient temperature and the sequence of the local time sequence of the wind speed according to the time dimension to obtain the sequence of the local time sequence input vector of the ambient temperature and the sequence of the local time sequence input vector of the wind speed.
It will be appreciated by those skilled in the art that the specific operations of the respective steps in the above-described intelligent electric fan control system based on the internet of things have been described in detail in the above description of the intelligent electric fan control method based on the internet of things with reference to fig. 1 to 2, and thus, repetitive descriptions thereof will be omitted.
As described above, the intelligent electric fan control system 200 based on the internet of things according to the embodiment of the present application may be implemented in various terminal devices, for example, a server for intelligent electric fan control based on the internet of things, and the like. In one example, the intelligent electric fan control system 200 based on the internet of things technology according to an embodiment of the present application may be integrated into a terminal device as one software module and/or hardware module. For example, the intelligent electric fan control system 200 based on the internet of things technology may be a software module in the operating system of the terminal device, or may be an application program developed for the terminal device; of course, the intelligent electric fan control system 200 based on the internet of things technology can also be one of a plurality of hardware modules of the terminal device.
Alternatively, in another example, the intelligent electric fan control system 200 based on the internet of things and the terminal device may be separate devices, and the intelligent electric fan control system 200 based on the internet of things may be connected to the terminal device through a wired and/or wireless network and transmit the interactive information according to an agreed data format.
Fig. 4 is an application scenario diagram of an intelligent electric fan control method based on the internet of things technology provided in an embodiment of the present application. As shown in fig. 4, in this application scenario, first, a time series of the ambient temperature acquired by the temperature sensor is acquired (e.g., C1 as illustrated in fig. 4); and, acquiring a time series of wind speeds acquired by a wind speed sensor (e.g., C2 as illustrated in fig. 4); then, the acquired time series of the ambient temperature and the time series of the wind speed are input into a server (e.g., S as illustrated in fig. 4) in which an intelligent electric fan control algorithm based on the internet of things technology is deployed, wherein the server is capable of processing the time series of the ambient temperature and the time series of the wind speed based on the intelligent electric fan control algorithm of the internet of things technology to determine a fan control instruction.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the application, and is not meant to limit the scope of the application, but to limit the application to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the application are intended to be included within the scope of the application.
Claims (10)
1. An intelligent electric fan control method based on the internet of things technology is characterized by comprising the following steps:
Acquiring a time sequence of ambient temperatures acquired by a temperature sensor;
Acquiring a time sequence of wind speeds acquired by a wind speed sensor;
Data normalization is carried out on the time sequence of the ambient temperature and the time sequence of the wind speed to obtain a sequence of the local time sequence input vector of the ambient temperature and a sequence of the local time sequence input vector of the wind speed;
Calculating a sample covariance correlation matrix between each group of corresponding local time sequence input vectors of the environment temperature and the local time sequence input vectors of the wind speed in the sequence of the local time sequence input vectors of the environment temperature and the local time sequence input vectors of the wind speed to obtain a sequence of temperature-wind speed correlation matrices in a local time domain;
determining a fan control instruction based on implicit correlation characteristics of the sequence of local time domain temperature-wind speed correlation matrices.
2. The intelligent electric fan control method based on the internet of things technology according to claim 1, wherein the data normalization is performed on the time series of the ambient temperature and the time series of the wind speed to obtain the sequence of the local time series input vector of the ambient temperature and the sequence of the local time series input vector of the wind speed, comprising:
performing sequence segmentation on the time sequence of the ambient temperature and the time sequence of the wind speed based on a preset time scale to obtain a sequence of a local time sequence of the ambient temperature and a sequence of a local time sequence of the wind speed;
and respectively normalizing the sequence of the local time sequence of the ambient temperature and the sequence of the local time sequence of the wind speed according to a time dimension to obtain the sequence of the local time sequence input vector of the ambient temperature and the sequence of the local time sequence input vector of the wind speed.
3. The intelligent electric fan control method based on the internet of things technology according to claim 2, wherein calculating a sample covariance correlation matrix between each set of corresponding ambient temperature local time sequence input vectors and wind speed local time sequence input vectors in the sequence of ambient temperature local time sequence input vectors and the sequence of wind speed local time sequence input vectors to obtain a sequence of temperature-wind speed correlation matrices in a local time domain, comprises:
Calculating a sample covariance matrix of the ambient temperature local time sequence input vector relative to the wind speed local time sequence input vector according to the following sample covariance correlation formula to obtain a temperature-wind speed correlation matrix in the local time domain; the sample covariance correlation formula is as follows: ; wherein/> Inputting a vector for the local timing of the ambient temperature,/>Inputting a vector for the local time sequence of wind speeds,/>And (5) the local time domain temperature-wind speed correlation matrix.
4. The internet of things-based intelligent electric fan control method of claim 3, wherein determining fan control instructions based on implicit correlation characteristics of the sequence of local time domain temperature-wind speed correlation matrices comprises:
Performing implicit feature extraction on each local time domain temperature-wind speed correlation matrix in the sequence of the local time domain temperature-wind speed correlation matrix by using a deep learning network model to obtain a sequence of local time domain temperature-wind speed correlation feature vectors;
The sequence of the temperature-wind speed associated characteristic vectors in the local time domain passes through an importance distribution network based on the self-adaptive attention module to obtain a weighted sequence of the temperature-wind speed associated characteristic vectors in the local time domain;
carrying out fusion optimization treatment on the sequence of the temperature-wind speed associated characteristic vectors in the local time domain and the sequence of the weighted local time domain to obtain an optimized full-time domain temperature-wind speed associated characteristic vector;
And the optimized full-time domain temperature-wind speed correlation characteristic vector is passed through a classifier to obtain the fan control instruction, wherein the fan control instruction is used for indicating whether the electric fan power at the current time point can be reduced.
5. The intelligent electric fan control method based on the internet of things according to claim 4, wherein the deep learning network model is a temperature-wind speed correlation feature extractor based on a convolutional neural network model.
6. The internet of things-based intelligent electric fan control method according to claim 5, wherein performing implicit feature extraction on each local time domain temperature-wind speed correlation matrix in the sequence of local time domain temperature-wind speed correlation matrices by using a deep learning network model to obtain the sequence of local time domain temperature-wind speed correlation feature vectors, comprises:
And passing each local time domain temperature-wind speed correlation matrix in the sequence of local time domain temperature-wind speed correlation matrices through the convolutional neural network model-based temperature-wind speed correlation feature extractor to obtain the sequence of local time domain temperature-wind speed correlation feature vectors.
7. The method for controlling an intelligent electric fan based on the internet of things according to claim 6, wherein the step of passing the sequence of the local time domain temperature-wind speed correlation eigenvectors through an importance distribution network based on an adaptive attention module to obtain the sequence of weighted local time domain temperature-wind speed correlation eigenvectors comprises:
Processing the sequence of the local time domain temperature-wind speed associated feature vectors with the following importance allocation formula to obtain the sequence of weighted local time domain temperature-wind speed associated feature vectors; wherein, the importance allocation formula is: ; wherein/> For the/>, in the sequence of temperature-wind speed associated feature vectors in the local time domainTemperature-wind speed correlation characteristic vector in local time domain,/>For pooling processing,/>For the purpose of pooling the vectors,Is a weight matrix,/>Is a bias vector,/>For the activation process,/>For the initial meta-weight feature vector,/>Is the/>, of the initial meta-weight feature vectorCharacteristic value of individual position,/>To correct the meta-weight feature vector,/>Is the/>, of the correction element weight feature vectorCharacteristic value of individual position,/>Is the/>, in the sequence of the weighted local time domain temperature-wind speed correlation eigenvectorsTemperature-wind speed associated characteristic vector in local time domain after weighting,/>The dot product process is represented.
8. The intelligent electric fan control method based on the internet of things according to claim 7, wherein the optimized full-time domain temperature-wind speed correlation feature vector is passed through a classifier to obtain the fan control instruction, where the fan control instruction is used to indicate whether the electric fan power at the current time point can be reduced, and the method includes:
performing full-connection coding on the optimized full-time domain temperature-wind speed associated feature vector by using a plurality of full-connection layers of the classifier to obtain a coded classification feature vector; and
And the coding classification feature vector is passed through a Softmax classification function of the classifier to obtain the classification result.
9. An intelligent electric fan control system based on internet of things technology, which is characterized by comprising:
The time sequence acquisition module of the ambient temperature is used for acquiring the time sequence of the ambient temperature acquired by the temperature sensor;
The time sequence acquisition module of the wind speed is used for acquiring the time sequence of the wind speed acquired by the wind speed sensor;
the data normalization module is used for performing data normalization on the time sequence of the ambient temperature and the time sequence of the wind speed to obtain a sequence of the local time sequence input vector of the ambient temperature and a sequence of the local time sequence input vector of the wind speed;
The sample covariance incidence matrix calculation module is used for calculating a sample covariance incidence matrix between each group of corresponding local time sequence input vectors of the environment temperature and the wind speed in the sequence of the local time sequence input vectors of the environment temperature and the wind speed so as to obtain a sequence of temperature-wind speed incidence matrices in a local time domain;
And the fan control instruction determining module is used for determining a fan control instruction based on implicit correlation characteristics of the sequence of the temperature-wind speed correlation matrix in the local time domain.
10. The intelligent electric fan control system based on the internet of things technology of claim 9, wherein the data normalization module comprises:
The sequence segmentation unit is used for carrying out sequence segmentation on the time sequence of the ambient temperature and the time sequence of the wind speed based on a preset time scale so as to obtain a sequence of a local time sequence of the ambient temperature and a sequence of a local time sequence of the wind speed;
And the sequence normalization unit is used for normalizing the sequence of the local time sequence of the ambient temperature and the sequence of the local time sequence of the wind speed according to the time dimension to obtain the sequence of the local time sequence input vector of the ambient temperature and the sequence of the local time sequence input vector of the wind speed.
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