CN110580943A - Intelligent infusion system based on LoRa communication - Google Patents

Intelligent infusion system based on LoRa communication Download PDF

Info

Publication number
CN110580943A
CN110580943A CN201910581225.3A CN201910581225A CN110580943A CN 110580943 A CN110580943 A CN 110580943A CN 201910581225 A CN201910581225 A CN 201910581225A CN 110580943 A CN110580943 A CN 110580943A
Authority
CN
China
Prior art keywords
lora communication
infusion
system based
repeater
monitoring unit
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201910581225.3A
Other languages
Chinese (zh)
Other versions
CN110580943B (en
Inventor
叶芝慧
张紫嫣
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing Tech University
Original Assignee
Nanjing Tech University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing Tech University filed Critical Nanjing Tech University
Priority to CN201910581225.3A priority Critical patent/CN110580943B/en
Publication of CN110580943A publication Critical patent/CN110580943A/en
Application granted granted Critical
Publication of CN110580943B publication Critical patent/CN110580943B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
    • A61M5/14—Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
    • A61M5/168—Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters ; Monitoring media flow to the body
    • A61M5/16831—Monitoring, detecting, signalling or eliminating infusion flow anomalies
    • A61M5/1684—Monitoring, detecting, signalling or eliminating infusion flow anomalies by detecting the amount of infusate remaining, e.g. signalling end of infusion
    • A61M5/16845—Monitoring, detecting, signalling or eliminating infusion flow anomalies by detecting the amount of infusate remaining, e.g. signalling end of infusion by weight
    • G—PHYSICS
    • G08—SIGNALLING
    • G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
    • G08C17/00—Arrangements for transmitting signals characterised by the use of a wireless electrical link
    • G08C17/02—Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G16H20/17—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
    • H04B5/77—
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
    • H04L12/44—Star or tree networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
    • H04L9/3271—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials using challenge-response
    • H04L9/3278—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials using challenge-response using physically unclonable functions [PUF]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M11/00—Telephonic communication systems specially adapted for combination with other electrical systems
    • H04M11/02—Telephonic communication systems specially adapted for combination with other electrical systems with bell or annunciator systems
    • H04M11/022—Paging systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M2205/00—General characteristics of the apparatus
    • A61M2205/18—General characteristics of the apparatus with alarm
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M2205/00—General characteristics of the apparatus
    • A61M2205/35—Communication
    • A61M2205/3546—Range
    • A61M2205/3553—Range remote, e.g. between patient's home and doctor's office
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention discloses an intelligent infusion system based on LoRa communication, which comprises an infusion monitoring unit, a pager, a repeater, a Lora communication module and a medical care station upper computer, wherein the infusion monitoring unit and the pager are connected with the repeater through the Lora communication module, and the medical care station upper computer is connected with the repeater through a network cable. According to the invention, the robustness and the safety of information transmission are improved through the LoRa wireless communication technology, and the accuracy of data measurement is improved through the weighing calibration module.

Description

Intelligent infusion system based on LoRa communication
Technical Field
The invention relates to the field of medical information, in particular to an intelligent infusion system based on Lora communication.
Background
The intelligent transfusion system can automatically judge the residual transfusion dosage, estimate the residual time, judge the situations of needle blockage, needle leakage, empty bottle, drip stop and the like, transmit the transfusion state information of each bed to the upper computer of the medical care station in real time through the Internet of things, enable nurses to see the transfusion process of each bed in the ward and replace the liquid medicine in time in the medical care station, and is an important research direction of intelligent medical treatment. The existing intelligent transfusion system mostly adopts Wifi and ZigBee for wireless communication, the transmission distance is small, and the intelligent transfusion system is easily influenced by interference.
Disclosure of Invention
The invention aims to provide an intelligent infusion system based on Lora communication.
The technical solution for realizing the purpose of the invention is as follows: the utility model provides an intelligent transfusion system based on Lora communication, includes infusion monitoring unit, repeater, calling set, wireless communication module and the host computer of doctorsing and nurses a station, infusion monitoring unit, calling set pass through Lora communication module and are connected with the repeater, the host computer of doctorsing and nurses a station passes through the net twine and is connected with the repeater. The transfusion monitoring unit is responsible for monitoring the weight of the liquid medicine, the transfusion progress and other information and transmitting the information to the repeater through the LoRa wireless communication technology; the caller is used for sending out an emergency call signal and transmitting the emergency call signal to the repeater through the LoRa wireless communication technology; the repeater acquires infusion related information from the infusion monitoring unit, acquires a calling signal from the caller, and transmits the calling signal to the upper computer of the medical care station through a network cable; the upper computer of the medical care station provides power for the repeater and sends out infusion ending reminding or calling reminding according to the information obtained from the repeater.
Compared with the prior art, the invention has the advantages that: 1) the invention improves the robustness and safety of information transmission by the LoRa wireless communication technology; 2) according to the invention, the weighing calibration module is arranged, so that the accuracy of data measurement is improved.
Drawings
Fig. 1 is a block diagram of a smart infusion system based on LoRa communication.
Fig. 2 is a flow chart of polling the Lora gateway.
Fig. 3 is a schematic diagram of a secure access Lora gateway.
Fig. 4 is a schematic view of a site in accordance with an embodiment of the present invention.
FIG. 5 is a specific flowchart of the wavelet neural network and the artificial fish swarm algorithm.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings.
As shown in figure 1, intelligent infusion system based on Lora communication, including infusion monitoring unit, repeater, calling set, wireless communication module and the host computer of medical care station, infusion monitoring unit, calling set pass through Lora communication module and are connected with the repeater, the host computer of medical care station passes through the net twine and is connected with the repeater. The transfusion monitoring unit is responsible for monitoring the weight of the liquid medicine, the transfusion progress and other information and transmitting the information to the repeater through the LoRa wireless communication technology; the caller is used for sending out an emergency call signal and transmitting the emergency call signal to the repeater through the LoRa wireless communication technology; the repeater acquires infusion related information from the infusion monitoring unit, acquires a calling signal from the caller, and transmits the calling signal to the upper computer of the medical care station through a network cable; the upper computer of the medical care station provides power for the repeater and sends out infusion ending reminding or calling reminding according to the information obtained from the repeater.
In some embodiments, the Lora communication module employs a star network architecture. Because the Lora gateway can only perform information intersection with the same terminal node at a certain time, in the process of wireless transmission, the Lora communication module adopts a polling mode to enable node data to access the Lora gateway in turn according to a certain period, and the polling flow is shown in fig. 2.
Generally, an infusion monitoring unit measures the weight of a medical fluid through a weighing sensor, thereby determining the infusion progress. However, the weighing sensor is easily affected by temperature to generate a zero drift phenomenon, so that the weighing information is inaccurate, and the alarm of the transfusion ending is advanced or lagged. In order to deal with the zero drift phenomenon generated by the weighing sensor under the influence of temperature, the invention arranges a weighing calibration module in the transfusion monitoring unit and adopts a wavelet neural network and an artificial fish swarm algorithm symmetrical retransmission sensor to compensate the zero temperature drift.
Before temperature compensation is carried out, firstly, voltage data output by weighing sensors under different conditions are collected through two modes of fixing the weight of liquid medicine, changing the temperature and fixing the temperature and changing the weight of the liquid medicine; then, learning the data sample by using a wavelet neural network, and gradually approximating the output of the neural network to an expected output value through training the wavelet neural network; because the wavelet neural network is easy to fall into local optimum, an artificial fish swarm algorithm is introduced to check the wavelet neural network so as to obtain a global optimum solution.
the specific process of the wavelet neural network and the artificial fish swarm algorithm is described as follows:
(1) Initializing parameters: for the number m of nodes of the input layer, the number n of nodes of the hidden layer, the number l of nodes of the output layer and the expansion factor a of the waveletjTranslation factor bjAnd the connection weight v between the input layer and the hidden layerjiThe connection weight w from the hidden layer to the output layerkjGiving initial value, selecting hidden layer neuron functionAnd an error function E;
(2) Starting wavelet neural network search: inputting a learning sample (gravity weighed by a weighing sensor under different temperature conditions) and corresponding expected output (voltage output by the weighing sensor), calculating the output of an output layer by using a formula (1), and calculating an error by using a formula (2);
Wherein M is the number of modes of the input sample,AndAre respectively provided withJ (j ═ 1,2, …, N) th desired output and actual output for the ith (i ═ 1,2, …, M) th mode;
(3) Judging whether to end the neural network algorithm: when the error is smaller than a certain preset threshold value, stopping the learning of the neural network, switching to an artificial fish swarm algorithm, otherwise, inputting a next sample, returning to the step 2, and continuing the learning of the neural network;
(4) Fish shoal initialization: initializing N artificial fishes by utilizing the output result of the wavelet neural network output layer, wherein the output result of the wavelet neural network is the state of the ith artificial fish, the error function in the step 2 is the adaptive value function of the artificial fish, the artificial fish schools are gathered at the same position, and the optimal solution on the bulletin board is the output result of the wavelet neural network;
(5) And (3) carrying out verification of the optimal solution: the method comprises the following steps that a fish school executes foraging behavior, a place with higher food concentration (namely a place with smaller adaptive value and smaller error) in a Visual field range (Visual) is searched according to a certain Step length (Step), if the position is not searched, the Step is randomly advanced one Step, the adaptive value of the artificial fish is changed, the optimal solution on a bulletin board is not updated, and when iteration is carried out for a certain number of times, the optimal solution of the bulletin board is not changed all the time, the output result of the wavelet neural network can be judged to be the global optimal result; if the fish school finds a place with higher food concentration in the foraging process, the optimal solution on the bulletin board is changed, and the result is fed back to the wavelet neural network to continue training, and the process is circulated until a global optimal solution is found.
In some embodiments, the Lora communication module is provided with a security verification access module, and the security verification access between the Lora communication module and the infusion monitoring unit and between the Lora communication module and the caller is implemented by using the property of a physical unclonable function of a chip, and a flow of the security verification access is shown in fig. 3, and specifically includes the following contents:
Because chip manufacturing processes in each infusion monitoring module and the calling device are different, the Lora gateway sends excitation signals to different infusion monitoring units and different calling devices respectively to obtain different response signals, and the responses are unique and can not be duplicated and can be used as the ID (identity) of each infusion monitoring unit or calling device, namely an identity verification code; the Lora gateway puts the identity verification codes into a storage module for ID access verification when the infusion monitor and the caller poll.
As shown in fig. 4, in some embodiments, the infusion monitoring unit and the beeper are disposed near the patient bed, and the repeater and the Lora communication module are disposed in the hospital corridor. Because the sickbeds are close to the infusion monitoring unit and the calling device, each sickbed is respectively bound with one infusion monitoring unit and one calling device by adopting a radio frequency identification technology. RFID labels are respectively arranged on sickbeds in infusion areas, each infusion monitor and the calling device are bound with the label on the sickbed, and after the labels are bound, the host of the medical care station can distinguish information sent by infusion monitoring units and calling devices from different beds.
To verify the validity of the inventive scheme, the following simulation experiment was performed.
lora communication test
In this embodiment, the Lora communication module is a wireless serial port module (UART) based on an SX1278 radio frequency chip manufactured by SEMTECH corporation, which is a product E32-433T20DC manufactured by eggbaud corporation, and the module uses TTL level. The default settings of the E32-433T20DC product are kept unchanged, namely the air transmission rate is 2.4kbps, the transmitting power is 20dBm, and the plane through-wall test and the cross-floor test are carried out in the environment with the presence of the interference of walls, electronic equipment, optical equipment and the like and including interference of 4G, WiFi, microwaves, millimeter waves and the like.
(1) plane through-wall testing
first, a transmission/reception test of data packets was performed in different rooms on the same floor, and the test results are shown in table 1 when 150000 transmission packets were transmitted. It can be seen that when the transmission path is as long as 60 meters (six walls apart), the packet loss rate is only 1.187%, which is higher than the communication quality in Wifi, ZigBee and other manners.
TABLE 1 test result table for plane wall penetration
Number of wall surfaces Number of transmission packets Number of lost packets packet loss rate
1(10m) 150000 416 0.277％
2(20m) 150000 425 0.283％
3(30m) 150000 478 0.319％
4(40m) 150000 579 0.386％
5(50m) 150000 1216 0.811％
6(60m) 150000 1780 1.187％
(2) Cross-floor testing
And respectively sending data packets to other floors by taking the third floor as a reference, wherein the test results are shown in a table 2. It can be seen that when the signal is transmitted to the sixth floor, the packet loss rate is only 1.271%, which is higher than the communication quality in Wifi, ZigBee and other manners.
TABLE 2 Cross-storey test result table
Partition floor Number of transmission packets Number of lost packets packet loss rate
1 (to four storied building) 120000 355 0.296％
2 (to five storied building) 120000 390 0.325％
3 (to six storied building) 120000 1525 1.271％
Second, weighing calibration test
In the weighing calibration module, a plurality of weighing sensors can be selected, different weighing sensors have different zero temperature drift rules due to differences in manufacturing processes, and therefore temperature compensation is performed according to the rules of the weighing sensors. Here, taking a PT14 type pressure sensor as an example, its output voltage data in various cases was measured, as shown in table 3.
TABLE 3 MEASUREMENT DATA TABLE FOR PT14 TYPE PRESSURE SENSOR
t(℃) P(KPa) 100 101 102 103 104 105 106 107 108
23.4 UP(mV) 21.6 43.0 61.8 81.0 99.1 118.9 139.2 153.4 183.5
30 UP(mV) 20.0 39.6 58.4 78.9 98.7 120.2 137.0 150.2 179.9
37 UP(mV) 18.8 39.4 58.9 77.9 95.4 113.9 133.3 152.8 173.9
44 UP(mV) 5.5 27.9 45.6 63.1 83.0 101.4 120.0 139.5 157.7
54 UP(mV) 7.9 27.2 46.4 63.5 84.5 102.4 120.0 139.4 158.3
60 UP(mV) 4.6 21.2 37.6 54.1 73.1 95.4 103.8 126.9 148.7
70 UP(mV) 4.9 18.5 31.3 49.6 67.4 86.9 96.8 118.9 137.3
In Table 3, t is the ambient temperature at which the pressure sensor is located, P is the pressure carried by the pressure sensor, UPIs the output voltage of the pressure sensor.
After the data acquisition is completed, the wavelet neural network and the artificial fish swarm algorithm are executed according to the graph 5. The parameter initialization comprises the following steps: setting 9 input layer nodes of the wavelet neural network to be 9 according to 9 values (100-108) of the pressure P, determining the number of modes of input samples to be 7 according to 7 values of the temperature t, and setting 1 output node to be used for outputting results; according to the experience of the existing research, the number of nodes of the hidden layer is set to be 16, the maximum training times of the network is set to be 500, the learning rate is 0.01, the momentum factor is 0.95, and the sum of squares of errors of training targets is 0.0001; the hidden layer neuron function adopts a Morlet wavelet function, and the function expression is as follows:
in addition, the size N of the fish school is set to 20, the Visual field Visual is set to 0.7, the Step size Step is set to 0.5, and the maximum number of iterations is 400. The above parameters are not intended to limit the present invention, and modifications and optimization may be made in the embodiments according to the circumstances.
Secondly, training a wavelet neural network, taking the gravity and the output voltage of the weighing sensor weighed under different temperature conditions as learning samples, calculating an output result by using a formula (1), and calculating an error by using a formula (2); and switching to an artificial fish school algorithm until the calculated error is smaller than a set threshold value, and taking the output result of the wavelet neural network as the initial state of the fish school.
Verifying whether the output is globally optimal by having the artificial fish swarm perform foraging behavior. Setting the current state of the ith artificial fish as XiCorresponding adaptation value is Yi(calculated by equation (2)), randomly selecting a state X within the Visual range thereofvIf the corresponding adaptive value Y is as in formula (4)v<YiIf the best solution on the bulletin board is updated to XvFeeding back to the wavelet neural network to enable the wavelet neural network to continue training so as to improve the result; otherwise, the artificial fish advances one step at random and changes to the state Xinexte.g., equation (5), without the bulletin board having to be updated.
Xv＝Xi+Rand()*Visual (4)
Xinext＝Xi+Rand()*Step (5)
And repeating the steps until the wavelet neural network outputs the optimal result, namely the temperature compensation achieves the optimal effect under the specified error.

Claims (6)

1. The utility model provides an intelligent transfusion system based on loRa communication which characterized in that: including infusion monitoring unit, calling set, repeater, Lora communication module and the host computer of doctorsing and nurses, infusion monitoring unit, calling set pass through Lora communication module and are connected with the repeater, the host computer of doctorsing and nurses passes through the net twine and is connected with the repeater.
2. The intelligent infusion system based on LoRa communication of claim 1, wherein: the Lora communication module adopts a star network structure, and in the information transmission process, node data are enabled to access the Lora gateway in turn by adopting a polling mode.
3. The intelligent infusion system based on LoRa communication of claim 1, wherein: the infusion monitoring unit is provided with a weighing calibration module, and the compensation of zero temperature drift is carried out by adopting a wavelet neural network and an artificial fish swarm algorithm symmetric retransmission sensor.
4. The intelligent infusion system based on LoRa communication of claim 1, wherein: the Lora communication module is provided with a safety verification access module, and the safety verification access of the Lora communication module, the infusion monitoring unit and the caller is realized by utilizing the physical unclonable function property of the chip.
5. The intelligent infusion system based on LoRa communication of claim 1, wherein: infusion monitoring unit and calling set up near the sick bed, repeater and Lora communication module set up in the hospital corridor.
6. The intelligent infusion system based on LoRa communication of claim 1, wherein: each sickbed corresponds to one infusion monitor and one calling device, RFID tags are arranged on the sickbeds in an infusion area respectively, and each infusion monitor and each calling device are bound with the tags on the sickbeds through a radio frequency identification technology.
CN201910581225.3A 2019-06-29 2019-06-29 Intelligent infusion system based on LoRa communication Active CN110580943B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910581225.3A CN110580943B (en) 2019-06-29 2019-06-29 Intelligent infusion system based on LoRa communication

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910581225.3A CN110580943B (en) 2019-06-29 2019-06-29 Intelligent infusion system based on LoRa communication

Publications (2)

Publication Number Publication Date
CN110580943A true CN110580943A (en) 2019-12-17
CN110580943B CN110580943B (en) 2022-08-12

Family

ID=68810822

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910581225.3A Active CN110580943B (en) 2019-06-29 2019-06-29 Intelligent infusion system based on LoRa communication

Country Status (1)

Country Link
CN (1) CN110580943B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112043916A (en) * 2020-09-30 2020-12-08 河南智互智能科技有限公司 Infusion monitoring system
CN112107763A (en) * 2020-08-11 2020-12-22 电子科技大学 Infusion monitoring system based on multisensor and wireless sensor network

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040130446A1 (en) * 2003-01-06 2004-07-08 Chen Thomas C. H. Wireless communication and global location enabled intelligent health monitoring system
CN109550112A (en) * 2019-01-22 2019-04-02 西安电子科技大学 Infusion monitoring system based on LoRa technology
CN109621091A (en) * 2019-02-15 2019-04-16 无锡识凌科技有限公司 Intelligent transfusion monitoring system

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040130446A1 (en) * 2003-01-06 2004-07-08 Chen Thomas C. H. Wireless communication and global location enabled intelligent health monitoring system
CN109550112A (en) * 2019-01-22 2019-04-02 西安电子科技大学 Infusion monitoring system based on LoRa technology
CN109621091A (en) * 2019-02-15 2019-04-16 无锡识凌科技有限公司 Intelligent transfusion monitoring system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112107763A (en) * 2020-08-11 2020-12-22 电子科技大学 Infusion monitoring system based on multisensor and wireless sensor network
CN112043916A (en) * 2020-09-30 2020-12-08 河南智互智能科技有限公司 Infusion monitoring system

Also Published As

Publication number Publication date
CN110580943B (en) 2022-08-12

Similar Documents

Publication Publication Date Title
CN110580943B (en) Intelligent infusion system based on LoRa communication
Pranata et al. Towards an IoT-based water quality monitoring system with brokerless pub/sub architecture
EP2991689B1 (en) Diffuser device, system and method for disinfection of spaces and surfaces
Hernández-Solana et al. Proposal and evaluation of BLE discovery process based on new features of Bluetooth 5.0
CA3139773A1 (en) Determining motion zones in a space traversed by wireless signals
De Venuto et al. Spatio-temporal optimization of perishable goods’ shelf life by a pro-active WSN-based architecture
Xu et al. Variation of received signal strength in wireless sensor network
CN107424381B (en) A kind of indoor monitoring system for Empty nest elderly
Lian et al. Mobile monitoring and embedded control system for factory environment
CN104469936A (en) Hybrid location method and system for wireless signal attenuation model based on intelligent space
Dolha et al. Considerations about the signal level measurement in wireless sensor networks for node position estimation
CN106372404A (en) Intelligent medical monitoring system
CN111897897B (en) Additional module, server device, positioning method, program, and storage medium
CN108957395A (en) The mobile target 3-D positioning method of noise immunity in a kind of tunnel
Kumar et al. An Efficient Aquaculture Monitoring Automatic System for Real Time Applications
Florencio et al. ISA 100.11 a networked control system based on link stability
Widodo et al. Performance evaluation of esp8266 for wireless nurse call system
Nugraha et al. Building of Anti-Bacterial Smart Sterilization Room Based on Internet of Things Using PIR Sensor and Its Quality Assurances
Korenivska et al. IoT monitoring system for microclimate parameters in educational institutions using edge devices
Kehua et al. An indoor positioning technology based on GA-BP neural network
CN108604399A (en) Occupy sensing system and method for sensing
Krupanek et al. Investigations of transmission delays in ZigBee networks
Robles-Enciso et al. Lora, zigbee and 5g propagation and transmission performance in an indoor environment at 868 mhz
Cui et al. Improving intelligence and efficiency of salt lake production by applying a decision support system based on IOT for brine pump management
Pérez-Solano et al. Optimizing the MAC protocol in localization systems based on IEEE 802.15. 4 networks

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant