CN113537459A - Method for predicting humiture of drug storage room - Google Patents
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Abstract
The invention discloses a medicine storehouse temperature and humidity prediction method based on Temporal _ ATT-Former, which comprises the following steps: acquiring historical data of temperature and humidity of a drug warehouse environment, and preprocessing the historical data; establishing a Temporal _ ATT-Former temperature and humidity prediction model; performing temperature and humidity prediction model training on the temperature _ ATT-Former according to the acquired temperature and humidity data characteristics of the drug storeroom; testing the effect of the Temporal _ ATT-Former model; and establishing a temperature and humidity early warning system of the drug storage room. The method accurately predicts the temperature and the humidity of the drug storage room in a certain time interval in the future, meets the requirement of storing drugs in the safe environment of the storage room, and has wide application prospect and practical value.
Description
Technical Field
The invention relates to a supervision algorithm and a time sequence prediction technology, in particular to a medicine storehouse temperature and humidity prediction method based on Temporal _ ATT-Former.
Background
At present, researchers can select mathematical modeling of collected temperature and humidity data and predict the temperature and humidity by combining statistics and machine learning methods, and the method has obvious defects, large calculation amount and low precision.
The existing paper mainly extracts collected temperature and humidity features based on the traditional recurrent neural network method to realize the prediction of the temperature and the humidity. For example, the method comprises the steps of Sezuku, Zhengduch, Bajun and Suzhongtang, prediction of temperature and humidity in the closed pigsty based on deep learning [ J/OL ], agricultural machinery science and newspaper, 2021, and establishment of a long-term and short-term memory network prediction model by combining historical data in the closed pigsty monitored by an actual temperature and humidity sensor. But the prediction speed of the model is reduced because the training speed of the long-short term memory network is slow and the model cannot be parallel. There is also a paper combining CNN and GRU to predict humiture, such as zhao quanming, songzao, liqifeng, zheng wenggang, liuyu, zhang bellelili. However, the CNN is not combined with the time series for losing part of the humiture characteristics when extracting the characteristics.
Traditional humiture monitoring of drug storage room environment is based on manual monitoring, and this kind of monitoring can not guarantee that the medicine is saved under specific humiture environment, consumes the cost of labor moreover.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a temperature and humidity prediction method for a drug storage room based on Temporal _ ATT-Former, which can realize accurate prediction of temperature and humidity data of the drug storage room and meet the requirement of safe storage of drugs in the storage room.
The technical scheme is as follows: the invention provides a medicine storehouse temperature and humidity prediction method based on Temporal _ ATT-Former, which comprises the following steps:
step 1: acquiring historical data of temperature and humidity of a drug warehouse environment, and preprocessing the historical data;
step 2: establishing a Temporal _ ATT-Former temperature and humidity prediction model;
and step 3: performing temperature and humidity prediction model training on the temperature _ ATT-Former according to the acquired temperature and humidity data characteristics of the drug storeroom;
and 4, step 4: testing the effect of the Temporal _ ATT-Former model;
and 5: and establishing a temperature and humidity early warning system of the drug storage room.
Further, historical data of the temperature and the humidity of the drug warehouse environment is acquired in the step 1, and the steps of preprocessing the historical data are as follows:
step 1.1: for each collection point of the drug storage room, a temperature and humidity sensor is used for collecting a first characteristic parameter TH of the drug storage room1The method comprises the steps of numbering a temperature and humidity sensor, the position of a collection point, temperature data, humidity data, a collected time sequence and alarm information;
step 1.2: preprocessing the first characteristic data, removing repeated data, filling up lost data to obtain second characteristic data TH2The method comprises the steps of collecting time sequence, temperature and humidity data and alarm information;
step 1.3: respectively acquiring daily, weekly and monthly cycle data of the temperature and humidity data of the time series obtained in the step 1.2, and collectingNormalizing and standardizing the data by a zero-mean method to obtain third characteristic data TH3。
Further, the specific steps of establishing the temperature and humidity prediction model of the Temporal _ ATT-Former in the step 2 are as follows:
step 2.1: the Temporal _ ATT-Former comprises an Encoder module and a Decoder module;
step 2.2: establishing an Encoder module which comprises an input layer, a convolutional layer, a time attention layer and a normalization and characteristic output layer;
step 2.3: and establishing a Decoder module which comprises an input layer, a multi-head time attention covering layer, a multi-head time attention layer, a full connection layer and an output layer.
Further, the Encoder module in step 2.2 is specifically:
a first layer: an input layer comprising temperature and humidity characteristics, local time series characteristics, and global time series characteristics, namely temperature and humidity input at the time t
Wherein the content of the first and second substances,
the characteristics of the temperature and the humidity are adopted,
is a local time series characteristic of the time series,
is a global time series feature;
a second layer: the convolution layer adopts one-dimensional convolution operation to respectively extract the characteristics of the humiture of the drug storage, namely
And a third layer: a multi-head time attention layer for strengthening the temperature and humidity characteristics of the time sequence of the input layer;
a fourth layer: and a normalization and feature output layer.
Further, the Decoder module in step 2.3 specifically is:
a first layer: input layer, input of temperature and humidity data shifted forward by T time stamps based on time series, i.e.
A second layer: an attention layer including a multi-temporal attention layer and a multi-temporal attention layer;
and a third layer: a fully-connected layer;
a fourth layer: and an output layer for outputting the predicted temperature and humidity data of the time series.
Further, the step 3 of performing temperature and humidity prediction model training on the Temporal _ ATT-Former according to the acquired temperature and humidity data characteristics of the drug warehouse comprises the following specific steps:
step 3.1: dividing a data set, namely dividing the temperature and humidity data set TH of the drug storage room preprocessed in the step 13Dividing a training set and a test set according to a certain proportion, and respectively expressing as train and test;
step 3.2: setting a training round epoch and a batch round batch;
step 3.3: training a humiture prediction model of a drug warehouse, inputting train into a Temporal _ ATT-Former network for training, and storing the trained model.
Further, the effect of the Temporal _ ATT-Former model is tested in the step 4, and the specific steps are as follows:
step 4.1: verifying the performance of the Temporal _ ATT-Former model, loading the stored model, and inputting the test into the model to obtain a prediction result r of the test set;
step 4.2: and comparing the temperature and humidity r predicted by the model with the real temperature and humidity value, calculating the average absolute error and the root mean square error, adjusting the parameters of the model, and optimizing the network structure.
Further, a temperature and humidity early warning system of the drug storage room is established in the step 5, and the method specifically comprises the following steps:
step 5.1: the temperature and humidity early warning system for the drug storage room comprises a temperature and humidity acquisition module, a storage module, a temperature and humidity prediction module and an early warning module;
step 5.2: the temperature and humidity acquisition module comprises temperature and humidity data of each acquisition point of the drug warehouse and transmits the temperature and humidity data to the server end;
step 5.3: the storage module is arranged at the server end and used for storing temperature and humidity data sent by the temperature and humidity sensor;
step 5.4: the temperature and humidity prediction module is used for cleaning, preprocessing, normalizing and standardizing temperature and humidity data, building a Temporal _ ATT-Former neural network, predicting the temperature and humidity value of a given time interval and transmitting the result to the cloud server end;
step 5.5: the early warning module judges a temperature and humidity prediction result in the cloud server, and if the temperature and humidity prediction result is greater than a certain set temperature and humidity threshold value, an early warning signal is sent to the client.
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages: according to the invention, firstly, the temperature and humidity data of the drug storage room are preprocessed, normalization and standardization processing are carried out on the data by adopting a zero-mean method, and supervised training is carried out on the collected temperature and humidity historical data of the drug storage room by utilizing a time attention mechanism and combining with an improved Transformer algorithm, so that the temperature and humidity prediction of the drug storage room with higher accuracy is realized, the prediction accuracy and speed are optimized, the manual monitoring cost can be reduced, and the drug can be stored for a long time in a safe environment.
Drawings
FIG. 1 is a general flow diagram of the present invention;
FIG. 2 is a model diagram of temperature and humidity prediction for constructing a Temporal _ ATT-Former pharmacy room in the invention;
FIG. 3 is a flowchart of the Temporal _ ATT-Former algorithm training and testing of the present invention;
fig. 4 is a flow chart of the temperature and humidity early warning system for the pharmaceutical warehouse in the invention.
Detailed Description
The invention is further elucidated with reference to the drawings and the embodiments.
As shown in fig. 1-4, a method for predicting temperature and humidity of a pharmacy based on Temporal _ ATT-Former includes the following steps:
step 1: acquiring historical data of temperature and humidity of a drug storage environment, and preprocessing the historical data, wherein the specific method comprises the following steps:
step 1.1: for each collection point of the drug storage room, a temperature and humidity sensor is used for collecting a first characteristic parameter TH of the drug storage room1The method comprises the steps of numbering a temperature and humidity sensor, the position of a collection point, temperature data, humidity data, a collected time sequence and alarm information;
step 1.2: preprocessing the first characteristic data, removing repeated data, filling up lost data to obtain second characteristic data TH2The method comprises the steps of collecting time sequence, temperature and humidity data and alarm information;
step 1.3: respectively acquiring cycle data of each day, each week and each month from the time series temperature and humidity data acquired in the step 1.2, and normalizing and standardizing the data by adopting a zero-mean method to obtain third characteristic data TH3。
Step 2: the method for establishing the temperature and humidity prediction model of the Temporal _ ATT-Former comprises the following specific steps:
step 2.1: the Temporal _ ATT-Former comprises an Encoder module and a Decoder module;
step 2.2: establishing an Encoder module which comprises an input layer, a convolutional layer, a time attention layer and a normalization and characteristic output layer;
a first layer: an input layer comprising temperature and humidity characteristics, local time series characteristics, and global time series characteristics, namely temperature and humidity input at the time t
Wherein
The characteristics of the temperature and the humidity are adopted,
is a local time series characteristic of the time series,
is a global time series feature.
A second layer: the convolution layer adopts one-dimensional convolution operation to respectively extract the characteristics of the humiture of the drug storage room, namely
And a third layer: and the multi-head time attention layer strengthens the temperature and humidity characteristics of the time sequence of the input layer.
A fourth layer: a normalization sum feature output layer;
step 2.3: establishing a Decoder module which comprises an input layer, a multi-head time attention layer, a full connection layer and an output layer;
a first layer: an input layer for inputting the temperature and humidity data based on time series by shifting the data forward by T time stamps
A second layer: an attention layer including a multi-temporal attention layer and a multi-temporal attention layer;
and a third layer: a fully-connected layer;
a fourth layer: and an output layer for outputting the predicted temperature and humidity data of the time series.
And step 3: performing temperature and humidity prediction model training on the acquired temperature and humidity data characteristics of the drug warehouse by using a Temporal _ ATT-Former, wherein the specific method comprises the following steps:
step 3.1: dividing a data set, namely dividing the temperature and humidity data set TH of the drug storage room preprocessed in the step 13Dividing a training set and a test set according to a certain proportion, and respectively expressing as train and test;
step 3.2: setting a training round epoch and a batch round batch;
step 3.3: training a humiture prediction model of a drug warehouse, inputting train into a Temporal _ ATT-Former network for training, and storing the trained model.
And 4, step 4: the method for testing the effect of the Temporal _ ATT-Former model comprises the following steps:
step 4.1: verifying the performance of the Temporal _ ATT-Former model, loading the stored model, and inputting the test into the model to obtain a prediction result r of the test set;
step 4.2: and comparing the temperature and humidity r predicted by the model with the real temperature and humidity value, calculating the average absolute error and the root mean square error, adjusting the parameters of the model, and optimizing the network structure.
And 5: the method for establishing the temperature and humidity early warning system of the drug storage room comprises the following steps:
step 5.1: the temperature and humidity early warning system for the drug storage room comprises a temperature and humidity acquisition module, a storage module, a temperature and humidity prediction module and an early warning module;
step 5.2: the temperature and humidity acquisition module comprises temperature and humidity data of each acquisition point of the drug storage room and transmits the temperature and humidity data to the server.
Step 5.3: the storage module is arranged at the server end and used for storing temperature and humidity data sent by the temperature and humidity sensor.
Step 5.4: the temperature and humidity prediction module is used for cleaning, preprocessing, normalizing and standardizing temperature and humidity data, building a Temporal _ ATT-Former neural network, predicting the temperature and humidity value of a given time interval and transmitting the result to the cloud server end;
step 5.5: the early warning module judges a temperature and humidity prediction result in the cloud server, and if the temperature and humidity prediction result is greater than a certain set temperature and humidity threshold value, an early warning signal is sent to the client.
In order to better illustrate the effectiveness of the method, 46088 pieces of humiture data of a drug depot for four months are subjected to normalization and standardization processing by adopting a zero-mean method, a time attention mechanism and an improved Transformer model are introduced to predict the humiture of the drug depot, the evaluation standard mean absolute error mae of the prediction model based on the Temporal _ ATT-Former is 0.172, and the root mean square error rmse is 0.231, so that the effect of the model is remarkably improved compared with that of the traditional machine learning and deep learning prediction algorithm.
The invention can be combined with a computer system so as to complete the automatic prediction of the humiture of the drug storage room.
The invention creatively provides a medicine storehouse temperature and humidity prediction method based on Temporal _ ATT-Former, and a medicine storehouse temperature and humidity prediction model is obtained through multiple experiments.
The medicine warehouse temperature and humidity prediction method based on Temporal _ ATT-Former can be used for medicine warehouse temperature and humidity prediction and can also be used for prediction based on other time prediction sequences.
Details not described herein are well within the skill of those in the art.
Claims (8)
1. A drug warehouse temperature and humidity prediction method based on Temporal _ ATT-Former is characterized by comprising the following steps:
step 1: acquiring historical data of temperature and humidity of a drug warehouse environment, and preprocessing the historical data;
step 2: establishing a Temporal _ ATT-Former temperature and humidity prediction model;
and step 3: performing temperature and humidity prediction model training on the temperature _ ATT-Former according to the acquired temperature and humidity data characteristics of the drug storeroom;
and 4, step 4: testing the effect of the Temporal _ ATT-Former model;
and 5: and establishing a temperature and humidity early warning system of the drug storage room.
2. The method for predicting the humiture of the pharmacy based on Temporal _ ATT-Former as claimed in claim 1, wherein the specific method in step 1 is as follows:
step 1.1: for each collection point of drug storeroomAcquiring a first characteristic parameter TH of the drug storage room by using a temperature and humidity sensor1The method comprises the steps of numbering a temperature and humidity sensor, the position of a collection point, temperature data, humidity data, a collected time sequence and alarm information;
step 1.2: preprocessing the first characteristic data, removing repeated data, filling up lost data to obtain second characteristic data TH2The method comprises the steps of collecting time sequence, temperature and humidity data and alarm information;
step 1.3: respectively acquiring cycle data of each day, each week and each month from the time series temperature and humidity data acquired in the step 1.2, and normalizing and standardizing the data by adopting a zero-mean method to obtain third characteristic data TH3。
3. The method for predicting the humiture of the pharmacy based on Temporal _ ATT-Former as claimed in claim 1, wherein the specific method in the step 2 is as follows:
step 2.1: the Temporal _ ATT-Former comprises an Encoder module and a Decoder module;
step 2.2: establishing an Encoder module which comprises an input layer, a convolutional layer, a time attention layer and a normalization and characteristic output layer;
step 2.3: and establishing a Decoder module which comprises an input layer, a multi-head time attention covering layer, a multi-head time attention layer, a full connection layer and an output layer.
4. The method for predicting the temperature and the humidity of the pharmacy based on the Temporal _ ATT-Former as claimed in claim 3, wherein the Encoder module is specifically:
a first layer: an input layer comprising temperature and humidity characteristics, local time series characteristics, and global time series characteristics, namely temperature and humidity input at the time t
Wherein the content of the first and second substances,
the characteristics of the temperature and the humidity are adopted,
is a local time series characteristic of the time series,
is a global time series feature;
a second layer: the convolution layer adopts one-dimensional convolution operation to respectively extract the characteristics of the humiture of the drug storage, namely
And a third layer: a multi-head time attention layer for strengthening the temperature and humidity characteristics of the time sequence of the input layer;
a fourth layer: and a normalization and feature output layer.
5. The method for predicting the temperature and the humidity of the pharmacy based on the Temporal _ ATT-Former as claimed in claim 3, wherein the Decoder module is specifically:
a first layer: input layer, input of temperature and humidity data shifted forward by T time stamps based on time series, i.e.
A second layer: an attention layer including a multi-temporal attention layer and a multi-temporal attention layer;
and a third layer: a fully-connected layer;
a fourth layer: and an output layer for outputting the predicted temperature and humidity data of the time series.
6. The method for predicting the humiture of the pharmacy based on Temporal _ ATT-Former as claimed in claim 1, wherein the specific method in step 3 is as follows:
step 3.1: dividing a data set, namely dividing the temperature and humidity data set TH of the drug storage room preprocessed in the step 13According to a certain proportionDividing a training set and a test set in proportion, and respectively representing as train and test;
step 3.2: setting a training round epoch and a batch round batch;
step 3.3: training a humiture prediction model of a drug warehouse, inputting train into a Temporal _ ATT-Former network for training, and storing the trained model.
7. The method for predicting the humiture of the pharmacy based on Temporal _ ATT-Former as claimed in claim 1, wherein the specific method in the step 4 is
Step 4.1: verifying the performance of the Temporal _ ATT-Former model, loading the stored model, and inputting the test into the model to obtain a prediction result r of the test set;
step 4.2: and comparing the temperature and humidity r predicted by the model with the real temperature and humidity value, calculating the average absolute error and the root mean square error, adjusting the parameters of the model, and optimizing the network structure.
8. The method for predicting the humiture of the pharmacy based on Temporal _ ATT-Former as claimed in claim 1, wherein the specific method in step 5 is as follows:
step 5.1: the temperature and humidity early warning system for the drug storage room comprises a temperature and humidity acquisition module, a storage module, a temperature and humidity prediction module and an early warning module;
step 5.2: the temperature and humidity acquisition module comprises temperature and humidity data of each acquisition point of the drug warehouse and transmits the temperature and humidity data to the server end;
step 5.3: the storage module is arranged at the server end and used for storing temperature and humidity data sent by the temperature and humidity sensor;
step 5.4: the temperature and humidity prediction module is used for cleaning, preprocessing, normalizing and standardizing temperature and humidity data, building a Temporal _ ATT-Former neural network, predicting the temperature and humidity value of a given time interval and transmitting the result to the cloud server end;
step 5.5: the early warning module judges a temperature and humidity prediction result in the cloud server, and if the temperature and humidity prediction result is greater than a certain set temperature and humidity threshold value, an early warning signal is sent to the client.
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