Remote medical equipment fault sensor monitoring system and method based on Internet of Things

Abstract

The invention discloses a remote medical equipment fault sensor monitoring system and method based on the Internet of Things, and relates to the technical field of medical equipment monitoring, the monitoring system comprises a data acquisition module, a storage module, a sensing data analysis module, a data processing module and an early warning and display module, the sensing data analysis module is used for judging whether the data collected by the sensors are abnormal or not, the data collection module is used for collecting data of medical equipment, different sensors are used for obtaining different patient data, and the data collection module transmits the data to the server through the Internet of Things. And the data processing module is used for predicting the monitoring data of the current fault sensor, so that the monitored sensor equipment can be quickly analyzed after the sensor in the medical equipment is abnormal when the monitoring system monitors the medical equipment, and the response speed of the monitoring system to the medical equipment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment monitoring, and in particular to a remote medical equipment fault sensor monitoring system and method based on the Internet of Things. Background Art

[0002] With the continuous development of medical technology, medical equipment plays a vital role in disease diagnosis, treatment and rehabilitation. Telemedicine equipment has enabled medical services to break through geographical restrictions, allowing patients to receive timely medical monitoring and diagnosis at home or in remote areas. However, once a medical device fails, it will not only affect the accuracy of diagnosis and treatment, but may even threaten the patient's life safety.

[0003] When monitoring sensors, the existing remote medical equipment fault sensor monitoring system may be unable to obtain verification of other data because the sensor can work normally but has some abnormalities. Therefore, we propose a remote medical equipment fault sensor monitoring system and method based on the Internet of Things. Summary of the invention

[0004] The object of the present invention is to provide a remote medical equipment fault sensor monitoring system and method based on the Internet of Things.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solutions: a remote medical equipment fault sensor monitoring system based on the Internet of Things, the monitoring system comprising a data acquisition module, a storage module, a sensor data analysis module, a data processing module and an early warning and display module;

[0006] The data acquisition module is used to collect data from medical equipment and use different sensors to obtain different patient data. The data acquisition module transmits the data to the server through the Internet of Things;

[0007] The storage module in the server stores the tree diagram data of the sensor types. After receiving the data from the data acquisition module, the server uses the tree diagram to analyze the associations between sensors of different types and transmits the sensor association data to the sensor data analysis module.

[0008] The sensor data analysis module is used to analyze the data between related sensors, and then determine the sensor failure according to the sensor data;

[0009] The data processing module is used to analyze the sensor fault state and predict the data of the current faulty sensor based on other associated sensors;

[0010] The warning and display module is used to warn of faulty sensors and display sensor data.

[0011] As a further solution of the present invention: a fault judgment unit is set in the sensor data analysis module, and the fault judgment unit is used to analyze the status of the faulty sensor. When the fault judgment unit analyzes that the faulty sensor can work normally but the data is abnormal due to other problems, the sensor data analysis module retrieves the associated sensor data and transmits it to the data processing module. When the fault judgment unit analyzes that the faulty sensor cannot work normally, the fault judgment unit transmits the sensor's fault data to the early warning and display module.

[0012] As a further solution of the present invention: after detecting a faulty sensor, the fault judgment unit creates sensor detection instruction data, and transmits the sensor detection instruction to the data acquisition module through the Internet of Things, and uses the data acquisition module to obtain the data of the faulty sensor, and then judges the operating status of the faulty sensor.

[0013] As a further solution of the present invention: a prediction model is set in the data processing module, and the prediction model is used to retrieve the associated sensor data to predict the fault sensor data, and the prediction data of the fault sensor is obtained by a formula. The specific formula is as follows:

[0014]

[0015] Among them, Z represents the monitoring data of the predicted fault sensor, X i represents the sensor regression coefficient associated with the faulty sensor, α i-1 represents the sensor monitoring data associated with the faulty sensor, and n represents the number of sensors associated with the faulty sensor.

[0016] As a further solution of the present invention: a data verification unit is set in the data processing module, and the data verification unit verifies the data of the faulty sensor, determines the accuracy of the monitoring data of the medical equipment, and then compares the predicted data of the prediction model with the data monitored by the faulty sensor, thereby verifying the fault state of the faulty sensor.

[0017] As a further solution of the present invention: a data sorting unit is set in the data processing module, and the data sorting unit is used to normalize the sensor monitoring data and the predicted sensor data, transmit the normalized data to the early warning and display module, and use the early warning and display module to display the normalized data.

[0018] As a further solution of the present invention: the early warning and display module is provided with multiple levels of early warning, and the early warning levels are respectively level one early warning, level two early warning and level three early warning. When the early warning and display module receives the data transmitted by the data processing module, it issues an early warning according to the data transmitted by the data processing module. When the data verification unit verifies that the data of the faulty sensor is abnormal, the early warning and display module issues a level one early warning reminder. When the data verification unit verifies that the data of the faulty sensor is not abnormal and the data processing module determines that the sensor is faulty, the early warning and display module issues a level two early warning reminder. When the sensor data analysis module does not determine that the sensor is faulty, the early warning and display module issues a level three early warning display. When the early warning and display module is a level two or level three early warning, it retrieves the normalized data in the data sorting unit for display, and marks and displays the predicted sensor data.

[0019] In addition, the present invention also provides a remote medical equipment fault sensor monitoring method, the specific steps are as follows:

[0020] Step 1: By setting different types of sensors on medical equipment and connecting different sensors to the Internet of Things for communication, the medical equipment data monitored by the sensors are transmitted to the server in real time through the Internet of Things;

[0021] Step 2: Using the storage module to store the monitoring data transmitted in real time in the server, and integrating the associated sensor data according to the tree diagram stored in the storage module, and then transmitting the sensor data to the sensor data analysis module;

[0022] Step 3: The sensor data analysis module analyzes the data associated with the sensors to determine whether the sensor data is abnormal. If an abnormality occurs, the sensor monitoring data related to the sensor is transmitted to the data processing module, and the data processing module further processes the sensor data. If no abnormality occurs, the data processing module integrates the sensor monitoring data, and then transmits the integrated data to the early warning and display module for display;

[0023] Step 4: When the data processing module receives abnormal data, the data processing module predicts the measurement data of the faulty sensor in a related manner, and then predicts the monitoring data of the sensor, and then uses the early warning and display module to perform early warning processing operations on it.

[0024] By adopting the above technical solution, compared with the prior art, the beneficial effects of the present invention are:

[0025] 1. The present invention obtains sensor data in real time through the Internet of Things, and then determines whether the data collected by the sensor is abnormal through the sensor data analysis module, so as to facilitate the judgment of whether the sensor is faulty, and then uses the data processing module to predict the monitoring data of the currently faulty sensor, so as to ensure that when the monitoring system monitors the medical equipment, the sensor in the medical equipment is abnormal and the monitored sensor device can be quickly analyzed, thereby improving the response speed of the monitoring system to the medical equipment;

[0026] 2. The present invention judges the sensor through the fault judgment unit, and then analyzes the data between the sensors according to the associated sensor data, which can quickly obtain whether abnormal data appears in the related sensors, and then analyzes according to the abnormal data, so that the monitoring system can quickly analyze the sensor in abnormal state;

[0027] 3. After the present invention obtains the faulty sensor through the data processing module, the data processing module uses the prediction module to analyze the sensor monitoring data associated with the faulty sensor, and then predicts the data of the faulty sensor, thereby achieving the effect of quickly analyzing the specific status of the medical device for the monitoring system;

[0028] 4. The present invention uses the early warning and display module to give prompts according to the early warning level, so that the staff can easily inspect and repair the sensor according to the early warning level. At the same time, the early warning and display module displays the type data of the sensor, which achieves the effect of facilitating rapid inspection and repair by the staff.

[0029] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 Schematic diagram of the monitoring system flow in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0032] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0033] Embodiment 1:

[0034] Please refer to the attached Figure 1, the present invention is a remote medical equipment fault sensor monitoring system based on the Internet of Things, the monitoring system includes a data acquisition module, a storage module, a sensor data analysis module, a data processing module and an early warning and display module;

[0035] The data acquisition module is used to collect data from medical equipment and use different sensors to obtain different patient data. The data acquisition module transmits the data to the server through the Internet of Things;

[0036] The server includes a storage module, the storage module is used to store the tree diagram data of the sensor types in the medical device, after receiving the data acquisition module, the server uses the tree diagram to analyze the correlation between different types of sensors, and transmits the sensor correlation data to the sensor data analysis module;

[0037] The sensor data analysis module is used to analyze the data between related sensors and then determine the sensor failure based on the sensor data;

[0038] The data processing module is used to analyze the sensor fault status and predict the data of the current faulty sensor based on other associated sensors;

[0039] The early warning and display module is used to warn of faulty sensors and display sensor data.

[0040] Specific workflow: By setting different types of sensors on medical equipment and connecting different sensors to the Internet of Things for communication, the medical equipment data monitored by the sensors is transmitted to the server in real time through the Internet of Things, and the monitoring data transmitted in real time in the server is stored by the storage module. At the same time, the associated sensor data is integrated according to the tree diagram stored in the storage module, and then the sensor data is transmitted to the sensor data analysis module. The sensor data analysis module analyzes the associated data between sensors to determine whether the sensor data is abnormal. When an abnormality occurs, the sensor monitoring data related to it is transmitted to the data processing module, and then the data processing module further processes the sensor data. When no abnormality occurs, the data processing module integrates the sensor monitoring data, and then transmits the integrated data to the early warning and display module for display. When the data processing module receives abnormal data, the data processing module predicts the measurement data of the faulty sensor in an associated manner, and then predicts the monitoring data of the sensor, and then uses the early warning and display module to perform early warning processing operations on it.

[0041] Furthermore, sensor data is acquired in real time through the Internet of Things, and then the sensor data analysis module is used to determine whether the data collected by the sensor is abnormal, which makes it easier to determine whether the sensor is faulty. The data processing module is then used to predict the monitoring data of the current faulty sensor, thereby ensuring that when the monitoring system is monitoring medical equipment, if the sensor in the medical equipment is abnormal, it can quickly analyze the monitored sensor equipment, thereby improving the response speed of the monitoring system to the medical equipment.

[0042] Embodiment 2:

[0043] Based on the first embodiment, please refer to the attached Figure 1 As shown, a fault judgment unit is set in the sensor data analysis module, and the fault judgment unit is used to analyze the status of the faulty sensor. When the fault judgment unit analyzes that the faulty sensor can work normally but the data is abnormal due to other problems, the sensor data analysis module retrieves the associated sensor data and transmits it to the data processing module. When the fault judgment unit analyzes that the faulty sensor cannot work normally, the fault judgment unit transmits the sensor's fault data to the early warning and display module. After detecting the faulty sensor, the fault judgment unit creates sensor detection instruction data, and transmits the sensor detection instruction to the data acquisition module through the Internet of Things. The data acquisition module is used to obtain the data of the faulty sensor, and then the operating status of the faulty sensor is judged.

[0044] Specific work flow: The fault judgment unit in the sensor data analysis module starts to analyze the status of the faulty sensor. The fault judgment unit will obtain the data of the faulty sensor. These data may be collected by the data acquisition module and stored after certain processing. If the fault judgment unit analyzes that the faulty sensor itself can work normally, but its data has deviations caused by other problems (for example, external interference, temporary errors in data transmission, etc.), the sensor data analysis module will retrieve the associated sensor data.

[0045] Furthermore, by judging the sensor through the fault judgment unit, and then analyzing the data between sensors based on the associated sensor data, it is possible to quickly obtain whether abnormal data appears in the related sensors, and then analyze based on the abnormal data, so that the monitoring system can quickly analyze the sensors in abnormal conditions.

[0046] Embodiment three:

[0047] Based on the second embodiment, please refer to the attached Figure 1 As shown, the data processing module includes a prediction model, which retrieves relevant sensor data and then uses the prediction model to predict the data of the faulty sensor. The prediction data of the faulty sensor is obtained through a formula. The specific formula is as follows:

[0048]

[0049] Among them, Z represents the monitoring data of the predicted fault sensor, X i represents the sensor regression coefficient associated with the faulty sensor, α i-1 represents the sensor monitoring data associated with the faulty sensor, n represents the number of sensors associated with the faulty sensor, a data verification unit is provided in the data processing module, the data verification unit verifies the data of the faulty sensor, determines the accuracy of the monitoring data of the medical device, and then compares the predicted data of the prediction model with the data monitored by the faulty sensor, thereby verifying the fault state of the faulty sensor, a data sorting unit is provided in the data processing module, the data sorting unit is used to normalize the sensor monitoring data and the predicted sensor data, transmit the normalized data to the early warning and display module, and use the early warning and display module to display the normalized data.

[0050] Specific workflow: The prediction model in the data processing module starts working. It will call the sensor data associated with the faulty sensor, and then use the formula to predict the monitoring data of the faulty sensor. The data will then be compared with the predicted data through the data verification unit. The fault state of the faulty sensor is verified by comparing the predicted data with the actual monitoring data. If the predicted data is significantly different from the actual monitoring data, it is further confirmed that the faulty sensor does have a fault, and then the sensor monitoring data and the predicted sensor data are normalized using the data sorting unit in the data processing module.

[0051] Furthermore, after the faulty sensor is acquired through the data processing module, the data processing module uses the prediction module to analyze the sensor monitoring data associated with the faulty sensor, and then predicts the data of the faulty sensor, thereby achieving the effect of quickly analyzing the specific status of the medical equipment for the monitoring system.

[0052] Embodiment 4:

[0053] Based on the third embodiment, please refer to the attached Figure 1As shown, the early warning and display module is provided with multiple levels of early warning, and the early warning levels are respectively level one, level two and level three. When the early warning and display module receives the data transmitted by the data processing module, it issues an early warning according to the data transmitted by the data processing module. When the data verification unit verifies that the data of the faulty sensor is abnormal, the early warning and display module issues a level one early warning reminder. When the data verification unit verifies that the data of the faulty sensor is not abnormal and the data processing module determines that the sensor is faulty, the early warning and display module issues a level two early warning reminder. When the sensor data analysis module does not determine that the sensor is faulty, the early warning and display module issues a level three early warning display. When the early warning and display module is a level two or level three early warning, it retrieves the normalized data in the data sorting unit for display, and marks and displays the predicted sensor data.

[0054] Specific work flow: When the data verification unit verifies that the data of the faulty sensor is abnormal, the early warning and display module issues a first-level early warning reminder; when the data verification unit verifies that the data of the faulty sensor is not abnormal, but the data processing module determines that the sensor is faulty, the early warning and display module issues a second-level early warning reminder; when the sensor data analysis module does not determine that the sensor is faulty, the early warning and display module performs a third-level early warning display; when the early warning and display module is a second-level early warning or a third-level early warning display, the early warning and display module retrieves the normalized data in the data sorting unit for display.

[0055] Furthermore, the warning and display module provides prompts according to the warning level, so that the staff can easily inspect and repair the sensor according to the warning level. At the same time, the warning and display module displays the type data of the sensor, which makes it easy for the staff to quickly inspect and repair.

[0056] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A remote medical equipment fault sensor monitoring system based on the Internet of Things, characterized by: The monitoring system includes a data acquisition module, a storage module, a sensor data analysis module, a data processing module, and an early warning and display module; The data acquisition module is used to collect data from medical equipment and use different sensors to obtain different patient data. The data acquisition module transmits the data to the server through the Internet of Things; The storage module in the server stores the tree diagram data of the sensor types. After receiving the data from the data acquisition module, the server uses the tree diagram to analyze the associations between sensors of different types and transmits the sensor association data to the sensor data analysis module. The sensor data analysis module is used to analyze the data between related sensors, and then determine the sensor failure according to the sensor data; The data processing module is used to analyze the sensor fault state and predict the data of the current faulty sensor based on other associated sensors; The warning and display module is used to warn of faulty sensors and display sensor data.

2. The remote medical equipment fault sensor monitoring system based on the Internet of Things according to claim 1 is characterized in that: A fault judgment unit is set in the sensor data analysis module, and the fault judgment unit is used to analyze the status of the faulty sensor. When the fault judgment unit analyzes that the faulty sensor can work normally but the data is abnormal due to other problems, the sensor data analysis module retrieves the associated sensor data and transmits it to the data processing module. When the fault judgment unit analyzes that the faulty sensor cannot work normally, the fault judgment unit transmits the sensor's fault data to the early warning and display module.

3. The remote medical equipment fault sensor monitoring system based on the Internet of Things according to claim 2 is characterized in that: After detecting a faulty sensor, the fault judgment unit creates sensor detection instruction data, transmits the sensor detection instruction to the data acquisition module through the Internet of Things, uses the data acquisition module to obtain the data of the faulty sensor, and then judges the operating status of the faulty sensor.

4. The remote medical equipment fault sensor monitoring system based on the Internet of Things according to claim 3 is characterized in that: The data processing module is provided with a prediction model, and the prediction model is used to retrieve the associated sensor data, and the fault sensor data is predicted. The prediction data of the fault sensor is obtained through a formula, and the specific formula is as follows: Among them, Z represents the monitoring data of the predicted fault sensor, X i represents the sensor regression coefficient associated with the faulty sensor, α i-1 represents the sensor monitoring data associated with the faulty sensor, and n represents the number of sensors associated with the faulty sensor.

5. The remote medical equipment fault sensor monitoring system based on the Internet of Things according to claim 4 is characterized in that: A data verification unit is provided in the data processing module, and the data verification unit verifies the data of the faulty sensor, determines the accuracy of the monitoring data of the medical device, and then compares the predicted data of the prediction model with the data monitored by the faulty sensor, thereby verifying the fault state of the faulty sensor.

6. The remote medical equipment fault sensor monitoring system based on the Internet of Things according to claim 5 is characterized in that: A data sorting unit is provided in the data processing module, and the data sorting unit is used to normalize the sensor monitoring data and the predicted sensor data, transmit the normalized data to the early warning and display module, and display the normalized data using the early warning and display module.

7. The remote medical equipment fault sensor monitoring system based on the Internet of Things according to claim 6 is characterized in that: The warning and display module is provided with multiple levels of warnings, and the warning levels are respectively level one, level two and level three. When the warning and display module receives the data transmitted by the data processing module, it issues a warning according to the data transmitted by the data processing module. When the data verification unit verifies that the data of the faulty sensor is abnormal, the warning and display module issues a level one warning reminder. When the data verification unit verifies that the data of the faulty sensor is not abnormal and the data processing module determines that the sensor is faulty, the warning and display module issues a level two warning reminder. When the sensor data analysis module does not determine that the sensor is faulty, the warning and display module issues a level three warning display. When the warning and display module is level two or level three warning, it retrieves the normalized data in the data sorting unit for display, and marks and displays the predicted sensor data.

8. A remote medical device fault sensor monitoring method applicable to any one of claims 1 to 7 of the remote medical device fault sensor monitoring system based on the Internet of Things, characterized in that: The specific steps are as follows: Step 1: By setting different types of sensors on medical equipment and connecting different sensors to the Internet of Things for communication, the medical equipment data monitored by the sensors are transmitted to the server in real time through the Internet of Things; Step 2: Using the storage module to store the monitoring data transmitted in real time in the server, and integrating the associated sensor data according to the tree diagram stored in the storage module, and then transmitting the sensor data to the sensor data analysis module; Step 3: The sensor data analysis module analyzes the data associated with the sensors to determine whether the sensor data is abnormal. If an abnormality occurs, the sensor monitoring data related to the sensor is transmitted to the data processing module, and the data processing module further processes the sensor data. If no abnormality occurs, the data processing module integrates the sensor monitoring data, and then transmits the integrated data to the early warning and display module for display; Step 4: When the data processing module receives abnormal data, the data processing module predicts the measurement data of the faulty sensor in a related manner, and then predicts the monitoring data of the sensor, and then uses the early warning and display module to perform early warning processing operations on it.

Images