CN113570156A - Thermal environment prediction model and thermal environment prediction method based on agricultural facilities - Google Patents

Thermal environment prediction model and thermal environment prediction method based on agricultural facilities Download PDF

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CN113570156A
CN113570156A CN202110950819.4A CN202110950819A CN113570156A CN 113570156 A CN113570156 A CN 113570156A CN 202110950819 A CN202110950819 A CN 202110950819A CN 113570156 A CN113570156 A CN 113570156A
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王洋
郭宇
张善宏
杨晋琪
李道亮
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Abstract

The invention provides a thermal environment prediction model and a thermal environment prediction method based on agricultural facilities, wherein the model comprises the following steps: the first memory module is used for extracting the characteristics of the time series information corresponding to the thermal environment to obtain the long-term space-time characteristics of the thermal environment, and the time series information comprises: an environmental parameter and a target parameter of the thermal environment; the second memory module is used for extracting short-term space-time characteristics of the thermal environment based on the long-term space-time characteristics; the autoregressive module is used for determining an initial prediction result based on the historical information of the target parameter; and the output module is used for outputting a final prediction result of the thermal environment based on the long-term space-time characteristic, the short-term space-time characteristic and the initial prediction result. The method is used for solving the defect that the effect is poor in the long-term prediction task of the thermal environment due to the fact that the characteristics of the thermal environment of the agricultural facility are not combined in the prior art.

Description

Thermal environment prediction model and thermal environment prediction method based on agricultural facilities
Technical Field
The invention relates to the technical field of agricultural facilities, in particular to a thermal environment prediction model and a thermal environment prediction method based on agricultural facilities.
Background
The prediction method for the thermal environment in facility agriculture (greenhouse, industrial fish and vegetable symbiosis, soilless culture and recirculating aquaculture) can be divided into mechanism model prediction, machine learning model prediction and time series model prediction. The latter two methods are called "black box models" because they do not care for the laws and principles of physics in the system. The application of these methods in mechanism predictive modeling will be described in detail below.
The mechanism model uses biological and physical principles to quantitatively analyze relevant factors in a system and establishes an equilibrium equation through the law of conservation of energy and mass. The mechanism model comprises a static model and a dynamic model: static models are also referred to as steady-state models; the dynamic model is usually described by a differential equation, which describes the law of change of the system. The earliest static model of the greenhouse environment was established by the student Businger, which laid the foundation for the later study. Although static models are easy to implement, they are less accurate. Thus, the japanese scholar Takakura et al established a first relatively complete kinetic model of an unheated single-layer glass greenhouse that fully describes the heat and moisture transfer processes in the greenhouse.
With the continuous improvement of the mechanism model, the method also brings convenience for the prediction and regulation of the agricultural facility environment. However, due to the large number of parameters and physical variables used by the mechanistic model, agricultural systems tend to be time-varying. Consequently, tuning is difficult in development and practice, and more researchers are beginning to focus on data-based models. And with the development of artificial intelligence, the computational performance and the data productivity are further improved, so that the application of a machine learning model in agricultural environment modeling is promoted.
Although the time series prediction can be realized by the prior art through a machine learning mode, the characteristics of the thermal environment of agricultural facilities are not combined, and the satisfactory effect cannot be achieved in the task of predicting the thermal environment for a long time. Therefore, how to realize accurate prediction of the thermal environment of agricultural facilities is an important issue to be solved in the industry at present.
Disclosure of Invention
The invention provides a thermal environment prediction model and a thermal environment prediction method based on agricultural facilities, which are used for solving the defect of poor effect in a long-term thermal environment prediction task caused by the fact that the characteristics of the thermal environment of the agricultural facilities are not combined in the prior art and realizing accurate prediction of the thermal environment.
The invention provides a thermal environment prediction model based on agricultural facilities, which comprises the following components: the first memory module is used for extracting the characteristics of time series information corresponding to the thermal environment to obtain the long-term space-time characteristics of the thermal environment, wherein the time series information comprises: an environmental parameter and a target parameter of the thermal environment;
a second memory module for extracting short-term spatiotemporal features of the thermal environment based on the long-term spatiotemporal features;
the autoregressive module is used for determining an initial prediction result based on the historical information of the target parameter;
an output module to output a final prediction result of the thermal environment based on the long-term spatiotemporal features, the short-term spatiotemporal features, and the initial prediction result.
According to the invention, the second memory module comprises: an attention unit and at least two second memory units;
the attention unit is used for connecting the first memory module and the second memory module, adjusting the long-term space-time characteristics, and transmitting the adjusted long-term space-time characteristics to the second memory unit.
According to the thermal environment prediction model based on the agricultural facility, the first memory module comprises at least two first memory units;
the attention unit is specifically configured to, after obtaining the long-term spatiotemporal feature, calculate an attention weight corresponding to each of the first memory units based on the long-term spatiotemporal feature and the short-term spatiotemporal feature, adjust the long-term spatiotemporal feature based on the attention weight, and transmit the adjusted long-term spatiotemporal feature to the second memory unit.
According to the thermal environment prediction model based on the agricultural facility, the second memory unit is used for determining the short-term space-time characteristics based on the adjusted long-term space-time characteristics and the historical information.
According to the agricultural facility-based thermal environment prediction model provided by the invention, the second memory module is a Recurrent Neural Network (RNN), and the second memory unit comprises: long and short term memory networks LSTM.
According to the invention, the first memory unit comprises: time domain convolutional network TCN.
According to the thermal environment prediction model based on the agricultural facility, the output module is specifically used for summing the long-term space-time characteristics, the short-term space-time characteristics and the initial prediction result to obtain the final prediction result.
The invention also provides a thermal environment prediction method based on the thermal environment prediction model of the agricultural facility, which comprises the following steps:
performing feature extraction on time series information corresponding to the thermal environment to obtain long-term space-time features of the thermal environment, wherein the time series information comprises: an environmental parameter and a target parameter of the thermal environment;
extracting short-term spatiotemporal features of the thermal environment based on the long-term spatiotemporal features;
determining an initial prediction result based on the historical information of the target parameter;
outputting a final prediction result of the thermal environment based on the long-term spatiotemporal features, the short-term spatiotemporal features, and the initial prediction result.
The method for predicting the thermal environment, provided by the invention, is used for extracting the short-term space-time characteristics of the thermal environment based on the long-term space-time characteristics, and comprises the following steps:
and adjusting the long-term spatiotemporal features, and determining the short-term spatiotemporal features based on the adjusted long-term spatiotemporal features and the historical information.
The thermal environment prediction method provided by the invention is characterized in that the final prediction result of the thermal environment is output based on the long-term space-time characteristics, the short-term space-time characteristics and the initial prediction result, and comprises the following steps:
and summing the long-term space-time characteristics, the short-term space-time characteristics and the initial prediction result to obtain the final prediction result.
The invention provides a thermal environment prediction model and a thermal environment prediction method based on agricultural facilities, which can obtain a prediction result of a thermal environment through the thermal environment prediction model, wherein the thermal environment prediction model comprises the following steps: the device comprises a first memory module, a second memory module, an autoregressive module and an output module. The invention carries out feature extraction on time series information corresponding to a thermal environment through a first memory module to obtain long-term space-time features of the thermal environment, wherein the time series information comprises: the thermal environment prediction model is effectively based on the relevant parameters of the thermal environment, and obtains the long-term space-time characteristics of the thermal environment; the second memory module extracts the short-term space-time characteristics of the thermal environment based on the long-term space-time characteristics of the thermal environment, so that the short-term space-time characteristics of the thermal environment are obtained; the autoregressive module is used for determining an initial prediction result based on the historical information of the target parameter; the output module is used for outputting the final prediction result of the thermal environment based on the long-term space-time characteristics, the short-term space-time characteristics and the initial prediction result.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic structural diagram of a thermal environment prediction model provided by the present invention;
FIG. 2 is a second schematic structural diagram of a thermal environment prediction model provided by the present invention;
FIG. 3 is a third schematic structural diagram of a thermal environment prediction model provided by the present invention;
FIG. 4 is a flow chart of a thermal environment prediction method provided by the present invention;
fig. 5 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The agricultural facility-based thermal environment prediction model of the present invention is described below in conjunction with fig. 1-3.
The embodiment of the invention provides a thermal environment prediction model based on agricultural facilities, a structural schematic diagram of the model is shown in figure 1, and the model specifically comprises:
the first memory module 101 is configured to perform feature extraction on time series information corresponding to a thermal environment to obtain long-term spatiotemporal features of the thermal environment, where the time series information includes: an environmental parameter and a target parameter of the thermal environment.
The second memory module 102 is configured to extract short-term spatiotemporal features of the thermal environment based on the long-term spatiotemporal features.
And the autoregressive module 103 is used for determining an initial prediction result based on the historical information of the target parameter.
And the output module 104 is used for outputting a final prediction result of the thermal environment based on the long-term space-time characteristic, the short-term space-time characteristic and the initial prediction result.
Wherein the long-term spatiotemporal features include: seasonal information, change trend information, temporal and spatial correlation and the like of the thermal environment; short-term spatiotemporal features include: short-term fluctuations in thermal environment, etc.
Specifically, the method is mainly aimed at the task of predicting the time series information of the thermal environment of the agricultural facility. Wherein the environmental parameters include: outdoor temperature, outdoor humidity, soil temperature, soil moisture, atmospheric pressure, indoor lighting, indoor carbon dioxide (CO)2) Concentration, indoor temperature, indoor humidity, circulating water temperature, and the like. Wherein the environmental parameters include: exogenous variables and target variables. The target variable corresponds to the final prediction result.
Then, the outdoor temperature, outdoor humidity, soil temperature, soil moisture, atmospheric pressure, indoor illumination, and indoor carbon dioxide (CO)2) The following description will be given, taking as examples the exogenous variables such as concentration, indoor temperature, indoor humidity, and circulating water temperature, and the target variables such as circulating water temperature:
wherein u is used for exogenous variable and target variable respectivelyt∈RNAnd vtE.g. R, which together form the input parameter z of the modelt＝{ut；vt}. The invention aims to predict target variables of H time steps in the future by using environmental parameters of historical T time steps, namely time sequence information. That is, the thermal environment prediction model establishes a mapping function f (-) of exogenous variables and target variables, see equation (1)
Figure BDA0003218566400000061
Namely, it is
Y＝f(X) (1)
Wherein the input is changedThe amount of the compound is X,
Figure BDA0003218566400000062
the predicted target variable is Y, Y ═ vT+1；vT+2；…；vT+H}∈RHT is a constant and H is a constant
In one embodiment, the first memory module 101 includes at least two first memory units, and the first memory units include: time domain Convolutional Network (TCN for short).
Specifically, due to the characteristics of strong coupling, large inertia, nonlinearity and the like of the facility agriculture thermal environment, data multivariable and long-time sequence need to be considered comprehensively when the model is created, and therefore the TCN model is adopted by the first memory module. Wherein the TCN model ensures that future information is not revealed by causal convolution. Furthermore, the TCN model can efficiently obtain space-time features of arbitrary length by deeper and expanding convolutions.
Therefore, the invention can capture long-term time series information and extract the long-term space-time characteristics of the thermal environment based on the long-term time series information.
Specifically, as shown in fig. 2, each TCN model includes 2 causal convolution TCN sub-modules 201, and each TCN sub-module includes: an extension convolutional layer 2011, a weight normalization layer 2012, an activation function (ReLU) 2013, and a Dropout layer 2014. The causal convolution refers to the convolution of the neuron with the time step t by using the information before the t-th time step in the previous layer, so that the leakage of the time sequence information can be effectively ensured. For a convolution kernel f of size 1 × k, the receptive field of the feature map Z exhibits a multiple increase compared to the general convolution module after adding the dilation convolution factor d, see equation (2). Further, the model has good generalization performance through weight normalization 2012, the ReLU activation function 2013 and the Dropout layer 2014. Finally, residual concatenation is deployed for the model by 1 × 1 convolution 202.
Figure BDA0003218566400000071
Wherein k is a constant, t is a constant, i is a constant, d is an expansion convolution factor, f is a convolution kernel, and Z is a result of each layer of convolution calculation.
Based on the TCN model, the invention extracts the long-term space-time characteristics of the thermal environment, and the long-term space-time characteristics at the time t are miThe long-term space-time characteristic of the TCN model output is expressed as M ═ { M ═ M1,m2,…,mT}∈RT×p。
In one embodiment, the second memory module 102 includes: an attention unit and at least two second memory units; and the attention unit is used for connecting the first memory module 101 and the second memory module 102, adjusting the long-term space-time characteristics, and transmitting the adjusted long-term space-time characteristics to the second memory unit.
In one embodiment, the second memory module 102 is a Recurrent Neural Network (RNN), and the second memory unit includes: long and short term memory networks LSTM.
The invention adopts the RNN network to effectively capture the short-term fluctuation in the thermal environment.
In one embodiment, the attention unit is specifically configured to, after obtaining the long-term spatiotemporal features, calculate an attention weight corresponding to each first memory unit based on the long-term spatiotemporal features and the short-term spatiotemporal features, adjust the long-term spatiotemporal features based on the attention weights, and transmit the adjusted long-term spatiotemporal features to the second memory unit.
Specifically, the invention effectively fuses the long-term space-time characteristics and the short-term space-time characteristics, so that the RNN can adaptively select a valuable first memory unit for reasoning. For example, at time t, the output result of the LSTM network hidden layer unit is output
Figure BDA0003218566400000072
Output m of TCN model as Query target (Query) of attention Uniti∈RpAs a query Key (Key) for attention units, and further, by
Figure BDA0003218566400000073
And miAttention weights are calculated, see formula (3) and formula (4):
Figure BDA0003218566400000081
Figure BDA0003218566400000082
wherein the hidden unit of the LSTM network outputs dt-1And st-1By mixing d witht-1And st-1Splicing to obtain
Figure BDA0003218566400000083
Wattn∈Rm×2q、Uattn∈Rm×pAnd vattn∈RmIs a training parameter for the attention unit,
Figure BDA0003218566400000084
as a query target for attention units, miAs a query key for attention units. Equation (4) for the intermediate energy value to be calculated for each query key value
Figure BDA0003218566400000085
Conversion to attention weight
Figure BDA0003218566400000086
Wherein, the attention weight may reflect the importance degree of the ith first memory unit to the model prediction target. LSTM network context information c of t time step after adaptive selection of first memory unit by modeltSee formula (5):
Figure BDA0003218566400000087
wherein, ctT is a constant value as an input parameter to the LSTM network.
In particular, ctI.e. the adjusted long-term spatio-temporal characteristics, ctTo the LSTM network.
In one embodiment, the second memory module 102 is specifically configured to determine the short-term spatiotemporal features based on the adjusted long-term spatiotemporal features and the historical information.
In particular, the long-term memory context information c processed by the attention unitt-1Historical information v of short time window with target variablet-1And (3) splicing the information to obtain the input information of the LSTM network, which is shown in a formula (6) and a formula (7):
Figure BDA0003218566400000088
Figure BDA0003218566400000089
wherein,
Figure BDA00032185664000000810
and
Figure BDA00032185664000000811
is a parameter of the training session that is,
Figure BDA00032185664000000812
denotes vt-1And ct-1Splicing result of fLSTMRepresents the forward propagation process of the LSTM network, and
Figure BDA00032185664000000813
for representing the output results, i.e., short-term spatio-temporal features, of the LSTM network.
Specifically, will
Figure BDA00032185664000000814
Input to LSTM network, output
Figure BDA00032185664000000815
In one embodiment, the output module 104 is specifically configured to sum the long-term spatio-temporal features, the short-term spatio-temporal features, and the initial prediction result to obtain a final prediction result.
Specifically, the obtaining manner of the initial prediction result is shown in formula (8):
Figure BDA0003218566400000091
wherein,
Figure BDA0003218566400000092
and barIs a parameter of the training session that is,
Figure BDA0003218566400000093
indicating the dependency on historical information vt-T+1,vt-T+2,…,vtSingle step initial prediction results. Obtaining an initial prediction result based on the single-step initial prediction result
Figure BDA0003218566400000094
The autoregressive module is used for solving the generalization problem of the prediction model, has higher precision on thermal environment prediction of the prediction model, and improves the generalization robustness of the prediction model.
Specifically, for the hidden results output by the TCN model and the RNN model, the long-term spatiotemporal features extracted by the TCN model and the short-term spatiotemporal features output by the RNN model are projected to the output latitude required by the thermal environment prediction task through a set of affine transformations, see formula (9) and formula (10):
Figure BDA0003218566400000095
Figure BDA0003218566400000096
wherein, Wtcn∈RH×T、Utcn∈Rp、btcn∈RH、Wrnn∈RH×2qAnd brnn∈RHAre training parameters. M ═ M1,m2,…,mT}∈RT×pRepresents the long-term spatio-temporal characteristics of the TCN model output,
Figure BDA0003218566400000097
representing the short-term spatio-temporal characteristics of the LSTM unit output at time t.
Finally, the local outputs of the TCN model, RNN model and autoregressive module 103 are summed to obtain the multi-step prediction result of the thermal environment, see formula (11). In addition, since the model is smooth and differentiable, the parameters in the predictive model can be trained by a back propagation algorithm, see equation (12):
Figure BDA0003218566400000098
Figure BDA0003218566400000099
where Θ represents all the training parameters in the model and N is the batch size in the mini-batch gradient descent algorithm. I | · | purple wind2Representing the final predicted outcome of model prediction
Figure BDA00032185664000000910
And real thermal environment data Y(i)L2 norm in between.
In addition, after the training of the prediction model is completed, the prediction accuracy of the model needs to be verified, which is specifically as follows:
in the invention, different evaluation strategies are provided from the aspects of the global performance index and the local performance index. Wherein the predicted effect of multiple future time steps is considered global performance, with emphasis on the overall prediction accuracy of the model, and is evaluated in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percent Error (MAPE), see equations (13), (14), and (15):
second, the degree of fit of each prediction step (i.e., single step evaluation in multi-step prediction results) is considered a local property, effectively reflecting how well the prediction results match real-world production. The relative square root error (RRSE) and the empirical correlation Coefficient (CORR) are used to measure the local performance of the model, see equations (16) and (17):
wherein,
Figure BDA0003218566400000101
is the model predicted value, y is the true measured value, the above index is defined as follows:
global performance index:
Figure BDA0003218566400000102
Figure BDA0003218566400000103
Figure BDA0003218566400000104
local performance index:
Figure BDA0003218566400000105
Figure BDA0003218566400000106
wherein i is a constant and represents any number of data samples, h represents the step size of multi-step prediction of the time series in the prediction model, N is a constant and represents the total amount of the data samples, and Ω represents the set of the data samples and the multi-step prediction result.
Wherein, for RMSE, MAE, MAPE and RRSE indexes, the larger the index, the better; for the CORR index, the smaller the index, the better.
The thermal environment prediction model of the present invention is described in detail below with reference to fig. 3:
firstly, connecting and serializing exogenous variables u and target variables v through a long historical information time window T, and obtaining long-term space-time characteristics m as input of a multilayer TCN modeli。
Then, the [ m ] is1,m2,…,mT]And
Figure BDA0003218566400000111
input into attention unit to obtain output c of attention unittWherein
Figure BDA0003218566400000112
is the hidden layer output, i.e., short-term spatio-temporal features, of the LSTM network.
Further, history information v of the short time window is extractedt-1And ct-1Input to the LSTM network.
Further, history information [ v ] of the long time window is stored1,v2,…,vT]The initial prediction result is input to the autoregressive module 103, and the initial prediction result output by the autoregressive module 103 is obtained.
And finally, inputting the long-term space-time characteristics, the short-term space-time characteristics and the initial prediction result into the output module 104 respectively, and obtaining a final prediction result through the output module 104.
In addition, through practical verification in a fish and vegetable symbiotic base, the thermal environment prediction model disclosed by the invention realizes excellent results in multi-step prediction of circulating water temperature. In the prediction tasks of 6-hour, 12-hour and 24-hour time spans, the RMSE indexes respectively achieve the accuracy improvement of 15.40%, 13.93% and 22.15%. In particular, the advantages in long-term prediction of thermal environments are more pronounced. The multi-scale memory structure gives the prediction model time sequence insight from coarse granularity to fine granularity, and can completely learn the rule in the thermal environment time sequence data. In addition, different components used by the method can effectively mine the relevant characteristics of time and space in the thermal environment data, so that the prediction model can acquire a sufficient time and space mode, and the accuracy of the prediction task is effectively improved.
The invention provides a thermal environment prediction model and a thermal environment prediction method based on agricultural facilities, which can obtain a prediction result of a thermal environment through the thermal environment prediction model, wherein the thermal environment prediction model comprises the following steps: the device comprises a first memory module, a second memory module, an autoregressive module and an output module. The invention carries out feature extraction on time series information corresponding to a thermal environment through a first memory module to obtain long-term space-time features of the thermal environment, wherein the time series information comprises: the thermal environment prediction model is effectively based on the relevant parameters of the thermal environment, and obtains the long-term space-time characteristics of the thermal environment; the second memory module extracts the short-term space-time characteristics of the thermal environment based on the long-term space-time characteristics of the thermal environment, so that the short-term space-time characteristics of the thermal environment are obtained; the autoregressive module is used for determining an initial prediction result based on the historical information of the target parameter; the output module is used for outputting the final prediction result of the thermal environment based on the long-term space-time characteristics, the short-term space-time characteristics and the initial prediction result.
The embodiment of the invention provides a thermal environment prediction method based on a thermal environment prediction model of agricultural facilities, wherein the thermal environment prediction method described below and the thermal environment prediction model described above are correspondingly referred to each other, and the specific implementation of the method is shown in fig. 4:
step 401, performing feature extraction on the time series information corresponding to the thermal environment to obtain long-term spatiotemporal features of the thermal environment.
Wherein the time series information includes: an environmental parameter and a target parameter of the thermal environment.
Step 402, extracting short-term spatiotemporal features of the thermal environment based on the long-term spatiotemporal features.
In one embodiment, the long-term spatiotemporal features are adjusted and the short-term spatiotemporal features are determined based on the adjusted long-term spatiotemporal features and historical information.
In step 403, an initial prediction result is determined based on the history information of the target parameter.
Step 404, outputting a final prediction result of the thermal environment based on the long-term spatiotemporal features, the short-term spatiotemporal features and the initial prediction result.
In one embodiment, the long-term spatiotemporal features, the short-term spatiotemporal features, and the initial prediction result are summed to obtain a final prediction result.
Fig. 5 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 5: a processor (processor)501, a communication Interface (Communications Interface)502, a memory (memory)503, and a communication bus 504, wherein the processor 501, the communication Interface 502, and the memory 503 are configured to communicate with each other via the communication bus 504. Processor 501 may call logic instructions in memory 503 to perform a thermal environment prediction method comprising: performing feature extraction on time series information corresponding to the thermal environment to obtain long-term space-time features of the thermal environment, wherein the time series information comprises: an environmental parameter and a target parameter of the thermal environment; extracting short-term space-time characteristics of the thermal environment based on the long-term space-time characteristics; determining an initial prediction result based on historical information of the target parameter; and outputting a final prediction result of the thermal environment based on the long-term space-time characteristics, the short-term space-time characteristics and the initial prediction result.
In addition, the logic instructions in the memory 503 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the thermal environment prediction method provided by the above methods, the method comprising: performing feature extraction on time series information corresponding to the thermal environment to obtain long-term space-time features of the thermal environment, wherein the time series information comprises: an environmental parameter and a target parameter of the thermal environment; extracting short-term space-time characteristics of the thermal environment based on the long-term space-time characteristics; determining an initial prediction result based on historical information of the target parameter; and outputting a final prediction result of the thermal environment based on the long-term space-time characteristics, the short-term space-time characteristics and the initial prediction result.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program that when executed by a processor is implemented to perform the thermal environment prediction methods provided above, the method comprising: performing feature extraction on time series information corresponding to the thermal environment to obtain long-term space-time features of the thermal environment, wherein the time series information comprises: an environmental parameter and a target parameter of the thermal environment; extracting short-term space-time characteristics of the thermal environment based on the long-term space-time characteristics; determining an initial prediction result based on historical information of the target parameter; and outputting a final prediction result of the thermal environment based on the long-term space-time characteristics, the short-term space-time characteristics and the initial prediction result.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. An agricultural facility-based thermal environment prediction model, comprising:
the first memory module is used for extracting the characteristics of time series information corresponding to the thermal environment to obtain the long-term space-time characteristics of the thermal environment, wherein the time series information comprises: an environmental parameter and a target parameter of the thermal environment;
a second memory module for extracting short-term spatiotemporal features of the thermal environment based on the long-term spatiotemporal features;
the autoregressive module is used for determining an initial prediction result based on the historical information of the target parameter;
an output module to output a final prediction result of the thermal environment based on the long-term spatiotemporal features, the short-term spatiotemporal features, and the initial prediction result.
2. The agricultural facility-based thermal environment prediction model of claim 1, wherein the second memory module comprises: an attention unit and at least two second memory units;
the attention unit is used for connecting the first memory module and the second memory module, adjusting the long-term space-time characteristics, and transmitting the adjusted long-term space-time characteristics to the second memory unit.
3. The agricultural facility-based thermal environment prediction model of claim 2, wherein the first memory module comprises at least two first memory units;
the attention unit is specifically configured to, after obtaining the long-term spatiotemporal feature, calculate an attention weight corresponding to each of the first memory units based on the long-term spatiotemporal feature and the short-term spatiotemporal feature, adjust the long-term spatiotemporal feature based on the attention weight, and transmit the adjusted long-term spatiotemporal feature to the second memory unit.
4. The agricultural facility-based thermal environment prediction model of claim 3, wherein the second memory unit is configured to determine the short-term spatiotemporal features based on the adjusted long-term spatiotemporal features and the historical information.
5. The agricultural facility-based thermal environment prediction model according to any one of claims 2-4, wherein the second memory module is a Recurrent Neural Network (RNN), and the second memory unit comprises: long and short term memory networks LSTM.
6. The agricultural facility-based thermal environment prediction model according to any one of claims 3-4, wherein the first memory unit comprises: time domain convolutional network TCN.
7. The agricultural facility-based thermal environment prediction model of claim 1, wherein the output module is specifically configured to sum the long-term spatiotemporal features, the short-term spatiotemporal features, and the initial prediction results to obtain the final prediction results.
8. A thermal environment prediction method based on the thermal environment prediction model of the agricultural facility as defined in any one of claims 1 to 7, comprising:
performing feature extraction on time series information corresponding to the thermal environment to obtain long-term space-time features of the thermal environment, wherein the time series information comprises: an environmental parameter and a target parameter of the thermal environment;
extracting short-term spatiotemporal features of the thermal environment based on the long-term spatiotemporal features;
determining an initial prediction result based on the historical information of the target parameter;
outputting a final prediction result of the thermal environment based on the long-term spatiotemporal features, the short-term spatiotemporal features, and the initial prediction result.
9. The thermal environment prediction method of claim 8, wherein the extracting short-term spatiotemporal features of the thermal environment based on the long-term spatiotemporal features comprises:
and adjusting the long-term spatiotemporal features, and determining the short-term spatiotemporal features based on the adjusted long-term spatiotemporal features and the historical information.
10. The method of predicting a thermal environment according to claim 9, wherein outputting a final prediction result of the thermal environment based on the long-term spatiotemporal features, the short-term spatiotemporal features, and the initial prediction result comprises:
and summing the long-term space-time characteristics, the short-term space-time characteristics and the initial prediction result to obtain the final prediction result.
CN202110950819.4A 2021-08-18 2021-08-18 Thermal environment prediction model and thermal environment prediction method based on agricultural facilities Pending CN113570156A (en)

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