CN112729411A - Distributed drug warehouse environment monitoring method based on GA-RNN - Google Patents

Distributed drug warehouse environment monitoring method based on GA-RNN Download PDF

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CN112729411A
CN112729411A CN202110046118.8A CN202110046118A CN112729411A CN 112729411 A CN112729411 A CN 112729411A CN 202110046118 A CN202110046118 A CN 202110046118A CN 112729411 A CN112729411 A CN 112729411A
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王逸之
杨忠
林敏�
张艳
余振中
满朝媛
周雨
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Hangzhou Nenggong Technology Co ltd
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Abstract

A distributed drug warehouse environment monitoring method based on GA-RNN. Aiming at the problems that the requirements of medicines on the temperature and the humidity of the environment of a storage warehouse are high, the quality of the medicines is easily damaged by abnormal temperature and humidity, and the like, the distributed type medicine warehouse environment monitoring method is designed, and temperature and humidity monitoring is carried out on each space of the medicine warehouse in real time by using a distributed type temperature and humidity monitoring instrument set. Comprises an upper computer and a lower computer. The lower computer part mainly comprises a distributed temperature and humidity monitor group and a wireless sensor, and the upper computer cloud platform is mainly used for data receiving, data analysis, data display, data early warning and the like. The upper computer platform firstly trains a genetic algorithm-recurrent neural network prediction model by using historical temperature and humidity information, then predicts the environment temperature by using temperature and humidity data acquired by a distributed temperature and humidity sensor in real time, and carries out abnormal temperature early warning by using the upper computer when the abnormal temperature and humidity condition is predicted to appear. So as to effectively early warn the abnormal state of the drug environment and ensure the drug safety.

Description

一种基于GA-RNN的分布式药品库房环境监测方法A distributed drug warehouse environment monitoring method based on GA-RNN

技术领域technical field

本发明涉及药品信息处理领域，特别设计一种基于GA-RNN的分布式药品库房环境监测方法。The invention relates to the field of drug information processing, and particularly designs a distributed drug storeroom environment monitoring method based on GA-RNN.

背景技术Background technique

生产药品及药品存储仓库的环境温湿度的正常对药品的安全存储是非常重要的，通常药品的存储环境的温湿度都是由监管人员实时观测，然而对于大型的药品存储库房，对温度的全天监测需要浪费大量的人力及财力，造成大量的资源浪费，此外，为了更安全的药品存储环境，仅仅对环境的监测是远远不够，必须对环境有一定的预判能力，这些对监管人员是非常困难的，因此，亟待提出一种药房智能监控系统，来保证药品的安全存储。The normal temperature and humidity of the production of drugs and drug storage warehouses are very important for the safe storage of drugs. Usually, the temperature and humidity of the drug storage environment are observed by supervisors in real time. Daily monitoring requires a lot of waste of manpower and financial resources, resulting in a lot of waste of resources. In addition, for a safer drug storage environment, only monitoring the environment is far from enough. It is necessary to have a certain ability to predict the environment. It is very difficult. Therefore, it is urgent to propose an intelligent monitoring system for pharmacies to ensure the safe storage of medicines.

发明内容SUMMARY OF THE INVENTION

为了解决上述存在问题。本发明提出一种基于GA-RNN的分布式药品库房环境监测方法。为达此目的：In order to solve the above problems. The invention proposes a distributed medicine warehouse environment monitoring method based on GA-RNN. For this purpose:

本发明提出一种基于GA-RNN的分布式药品库房环境监测方法，其特征在于：The present invention proposes a GA-RNN-based distributed drug warehouse environment monitoring method, which is characterized in that:

步骤1：把历史温湿度传感器组采集的数据集S上传到上位机；Step 1: Upload the data set S collected by the historical temperature and humidity sensor group to the host computer;

步骤2：使用历史温度集训练GA-RNN分布式预测模型；Step 2: Use the historical temperature set to train the GA-RNN distributed prediction model;

步骤3：使用训练好的GA-RNN分布式预测模型对仓库实时采集温湿度进行预测；Step 3: Use the trained GA-RNN distributed prediction model to predict the real-time collection of temperature and humidity in the warehouse;

步骤4：如果预测温湿度出现异常，则开启警报系统，否则，在上位机显示界面正常显示温度波形。Step 4: If the predicted temperature and humidity are abnormal, turn on the alarm system, otherwise, display the temperature waveform normally on the display interface of the host computer.

作为本发明的进一步改进，所述步骤1中数据集S包含N个分布式传感器采集的温湿度数据可表示为：As a further improvement of the present invention, in the step 1, the data set S includes the temperature and humidity data collected by N distributed sensors, which can be expressed as:

S＝[S1 S2 ... SN] (1)S=[S 1 S 2 ... S N ] (1)

Figure BDA0002897398370000011
Figure BDA0002897398370000011

其中，

Figure BDA0002897398370000012
表示采集温度数据组，
Figure BDA0002897398370000013
表示采集湿度数据组，k表示数据长度，且
Figure BDA0002897398370000021
in,
Figure BDA0002897398370000012
represents the collection of temperature data sets,
Figure BDA0002897398370000013
represents the collected humidity data group, k represents the data length, and
Figure BDA0002897398370000021

作为本发明的进一步改进，所述步骤2中GA-RNN模型训练分为RNN组训练和GA权重训练，RNN组训练表示为：As a further improvement of the present invention, the GA-RNN model training in the step 2 is divided into RNN group training and GA weight training, and the RNN group training is expressed as:

Figure BDA0002897398370000022
Figure BDA0002897398370000022

其中，RNN(·)表示RNN训练模型，

Figure BDA0002897398370000023
Among them, RNN( ) represents the RNN training model,
Figure BDA0002897398370000023

作为本发明的进一步改进，所述步骤2GA权重训练表示为：As a further improvement of the present invention, the step 2GA weight training is expressed as:

P＝GA([R1 R2 ... RN]T) (4)P=GA([R 1 R 2 ... R N ] T ) (4)

P＝[P1 P2 ... PN] (5)P=[P 1 P 2 ... P N ] (5)

Figure BDA0002897398370000024
Figure BDA0002897398370000024

其中，GA(·)是遗传算法寻优函数，Pi表示第i个传感器组所占权重，|| ||F表示计算向量的Frobenius范数。Among them, GA(·) is the genetic algorithm optimization function, Pi represents the weight of the i -th sensor group, and || || F represents the Frobenius norm of the calculation vector.

作为本发明的进一步改进，所述步骤3温湿度异常情况判别表示为：As a further improvement of the present invention, the abnormal condition of temperature and humidity in the step 3 is discriminated and expressed as:

Figure BDA0002897398370000025
Figure BDA0002897398370000025

Figure BDA0002897398370000026
Figure BDA0002897398370000026

w＝max([RNN(S1) RNN(S2) ... RNN(SN)])·0.6 (9)w=max([RNN(S 1 ) RNN(S 2 ) ... RNN(S N )])·0.6 (9)

其中,w为温湿度异常判断阈值，H＝1时，表示温湿度判断异常，max(·)表示求最大值函数。Among them, w is the judgment threshold of abnormal temperature and humidity, when H=1, it means that the judgment of temperature and humidity is abnormal, and max(·) represents the function of finding the maximum value.

本发明一种基于GA-RNN的分布式药品库房环境监测方法，有益效果在于：A GA-RNN-based distributed medicine warehouse environment monitoring method of the present invention has the following beneficial effects:

1.本发明利用分布式温湿度传感器组，能够对库房温度全方位监测。1. The present invention utilizes distributed temperature and humidity sensor groups, which can monitor the temperature of the warehouse in an all-round way.

2.本发明建立GA-RNN温湿度预测模型，增减了温湿度预测的鲁棒性与准确度。2. The present invention establishes a GA-RNN temperature and humidity prediction model, which increases or decreases the robustness and accuracy of temperature and humidity prediction.

3.本发明算法复杂度低，实时性强。3. The algorithm of the present invention has low complexity and strong real-time performance.

4.本发明硬件系统实现简单，成本低。4. The hardware system of the present invention is simple in implementation and low in cost.

附图说明Description of drawings

图1系统流程图。Figure 1 system flow chart.

具体实施方式Detailed ways

本发明提出一种基于GA-RNN的分布式药品库房环境监测方法。The invention proposes a distributed medicine warehouse environment monitoring method based on GA-RNN.

下面结合附图与具体实施方式对本发明进一步描述：The present invention is further described below in conjunction with the accompanying drawings and specific embodiments:

如图1所示，本发明提出一种基于GA-RNN的分布式药品库房环境监测方法主要分为两个部分，首先是离线GA-RNN模型训练阶段，把历史温湿度传感器组采集的数据集S上传到上位机；使用历史温度集训练GA-RNN分布式预测模型，其中，N＝128，k＝512。As shown in Figure 1, the present invention proposes a GA-RNN-based distributed drug warehouse environment monitoring method which is mainly divided into two parts. The first is the offline GA-RNN model training phase, where the data sets collected by the historical temperature and humidity sensor groups are collected. S is uploaded to the upper computer; the GA-RNN distributed prediction model is trained using the historical temperature set, where N=128, k=512.

数据集S包含N个分布式传感器采集的温湿度数据可表示为：The data set S contains the temperature and humidity data collected by N distributed sensors, which can be expressed as:

S＝[S1 S2 ... SN] (1)S=[S 1 S 2 ... S N ] (1)

Figure BDA0002897398370000031
Figure BDA0002897398370000031

其中，

Figure BDA0002897398370000032
表示采集温度数据组，
Figure BDA0002897398370000033
表示采集湿度度数据组，k表示数据长度，且
Figure BDA0002897398370000034
in,
Figure BDA0002897398370000032
represents the collection of temperature data sets,
Figure BDA0002897398370000033
represents the collected humidity data group, k represents the data length, and
Figure BDA0002897398370000034

GA-RNN模型训练分为RNN组训练和GA权重训练，RNN组训练表示为：The GA-RNN model training is divided into RNN group training and GA weight training. The RNN group training is expressed as:

Figure BDA0002897398370000035
Figure BDA0002897398370000035

其中，RNN(·)表示RNN训练模型，

Figure BDA0002897398370000036
Among them, RNN( ) represents the RNN training model,
Figure BDA0002897398370000036

GA权重训练表示为：GA weight training is expressed as:

P＝GA([R1 R2 ... RN]T) (4)P=GA([R 1 R 2 ... R N ] T ) (4)

P＝[P1 P2 ... PN] (5)P=[P 1 P 2 ... P N ] (5)

Figure BDA0002897398370000037
Figure BDA0002897398370000037

其中，GA(·)是遗传算法寻优函数，Pi表示第i个传感器组所占权重，|| ||F表示计算向量的Frobenius范数。Among them, GA(·) is the genetic algorithm optimization function, Pi represents the weight of the i -th sensor group, and || || F represents the Frobenius norm of the calculation vector.

最后是GA-RNN模型实时监测阶段，使用训练好的GA-RNN分布式预测模型对仓库实时采集温湿度进行预测；如果预测温湿度出现异常，则开启警报系统，否则，在上位机显示界面正常显示温度波形。Finally, in the real-time monitoring stage of the GA-RNN model, the trained GA-RNN distributed prediction model is used to predict the real-time collection of temperature and humidity in the warehouse; if the predicted temperature and humidity are abnormal, the alarm system is turned on, otherwise, the display interface on the host computer is normal. Displays the temperature waveform.

温湿度异常情况判别表示为：The judgment of abnormal temperature and humidity is expressed as:

Figure BDA0002897398370000041
Figure BDA0002897398370000041

Figure BDA0002897398370000042
Figure BDA0002897398370000042

w＝max([RNN(S1) RNN(S2) ... RNN(SN)])·0.6 (9)w=max([RNN(S 1 ) RNN(S 2 ) ... RNN(S N )])·0.6 (9)

其中,w为温湿度异常判断阈值，H＝1时，表示温湿度判断异常，max(·)表示求最大值函数。Among them, w is the judgment threshold of abnormal temperature and humidity, when H=1, it means that the judgment of temperature and humidity is abnormal, and max(·) represents the function of finding the maximum value.

以上所述，仅是本发明的较佳实施例而已，并非是对本发明作任何其他形式的限制，而依据本发明的技术实质所作的任何修改或等同变化，仍属于本发明所要求保护的范围。The above are only preferred embodiments of the present invention, and are not intended to limit the present invention in any other form, and any modifications or equivalent changes made according to the technical essence of the present invention still fall within the scope of protection of the present invention. .

具体实施例需要提及所有附图Specific embodiments need to refer to all figures

Claims (5)

1. A GA-RNN-based distributed drug warehouse environment monitoring method comprises the following specific steps:
step 1: uploading a data set S collected by a historical temperature and humidity sensor group to an upper computer;
step 2: training a GA-RNN distributed prediction model by using a historical temperature set;
and step 3: forecasting real-time temperature and humidity collection of the warehouse by using a trained GA-RNN distributed forecasting model;
and 4, step 4: if the temperature and the humidity are predicted to be abnormal, an alarm system is started, otherwise, the temperature waveform is normally displayed on a display interface of the upper computer.
2. The distributed GA-RNN-based drug storage environment monitoring method according to claim 1, wherein the method comprises the following steps:
in the step 1, the data set S includes temperature and humidity data acquired by N distributed sensors, which may be represented as:
S＝[S1 S2 ... SN] (1)
Figure FDA0002897398360000011
wherein,
Figure FDA0002897398360000012
it is indicated that the temperature data set is collected,
Figure FDA0002897398360000013
indicating the collection of a humidity data set, k indicates the data length, and
Figure FDA0002897398360000014
3. the distributed GA-RNN-based drug storage environment monitoring method according to claim 1, wherein the method comprises the following steps:
in the step 2, the GA-RNN model training is divided into RNN group training and GA weight training, wherein the RNN group training is represented as:
Figure FDA0002897398360000015
wherein RNN (-) represents the RNN training model,
Figure FDA0002897398360000018
4. the distributed GA-RNN-based drug storage environment monitoring method according to claim 1, wherein the method comprises the following steps:
the step 2GA weight training is represented as:
P＝GA([R1 R2 ... RN]T) (4)
P＝[P1 P2 ... PN] (5)
Figure FDA0002897398360000016
wherein GA (-) is a genetic algorithm optimization function, PiRepresents the weight occupied by the ith sensor group, | | | | | non-woven phosphorFRepresenting the Frobenius norm of the calculated vector.
5. The distributed GA-RNN-based drug storage environment monitoring method according to claim 1, wherein the method comprises the following steps:
and 3, judging the abnormal temperature and humidity conditions in the step:
Figure FDA0002897398360000017
Figure FDA0002897398360000021
w＝max([RNN(S1) RNN(S2) ... RNN(SN)])·0.6 (9)
where w is a temperature and humidity abnormality determination threshold, and when H is 1, it indicates that the temperature and humidity determination is abnormal, and max (·) indicates a maximum value calculation function.
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