Medical equipment operation state analysis method and system based on Internet of Things
Through the Internet of Things-based medical equipment operation status analysis method, the status monitoring data is used to determine the equipment status, and the problem of low efficiency of hospital equipment inspection is solved, timely discovery and accurate analysis of equipment status is achieved, and the reliable operation of equipment is ensured.
Patent Information
- Application Number
- CN202411210906.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-30
- Publication Date
- 2024-12-20
AI Technical Summary
There are a wide variety of equipment in the hospital and numerous. The existing technology relies on fixed-cycle visual inspection and parameter measurement, which is inefficient, making it difficult to detect equipment failures in a timely manner, resulting in economic property losses.
Using the Internet of Things-based medical equipment operation status analysis method, the high representative data value and low representative data value are determined by obtaining the status monitoring data carrying timestamps. Combined with the pre-acquisition standard data value range, the operating status of the equipment is determined to realize the equipment. Reasonable and accurate analysis of the status.
It realizes timely detection of equipment abnormalities, improves the reliable operation of equipment, reduces economic property losses, and improves the efficiency and accuracy of equipment maintenance.
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Figure CN119170233A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of equipment analysis, and in particular to a method and system for analyzing the operating status of medical equipment based on the Internet of Things. Background Art
[0002] The operating status of equipment refers to the status of the equipment during use. Generally speaking, in order to ensure the normal operation of the equipment, the operating status of the equipment needs to be monitored. Different places have different means of monitoring equipment. For places like hospitals, there are many types of equipment and a large number of them, so the equipment needs to be inspected and repaired at regular intervals. Each time the equipment in the hospital is inspected and repaired, the method used is to conduct on-site visual inspections and key parameter measurements of each device at a fixed period, and then choose whether to repair and the means of repair based on the measurement results. This method takes a long time and is inefficient. If an equipment abnormality occurs during the non-inspection period, the equipment often fails to work properly or even paralyzed due to the inability to detect the fault in time, causing irreparable economic and property losses to the hospital. Summary of the invention
[0003] The present application provides a method and system for analyzing the operating status of medical equipment based on the Internet of Things, which is conducive to ensuring the reliable operation of the equipment.
[0004] In a first aspect, the present application provides a method comprising: Acquire status monitoring data of the device, wherein the status monitoring data carries a timestamp and a collection value; Determine a high representative data value and a low representative data value according to the state monitoring data, wherein the high representative data value is associated with the highest value of the collected values of the state monitoring data in the operation cycle, and the low representative data value is associated with the lowest value of the collected values of the state monitoring data in the operation cycle; The operating status result of the device is determined according to the high representative data value, the low representative data value and the pre-acquired standard data value range.
[0005] By adopting the above technical solution, the operating status of the equipment can be reasonably analyzed, which is conducive to reasonably and accurately determining the operating status results of the equipment, and further conducive to ensuring the reliable operation of the equipment.
[0006] Further, determining a high representative data value and a low representative data value according to the status monitoring data includes: Analyze the collected values of the status monitoring data based on the timestamp to obtain the highest and lowest collected values of each operation cycle. If the collected value of the latter status monitoring data in two adjacent status monitoring data is lower than the collected value of the former status monitoring data, the collected value of the former status monitoring data is the highest value of the previous operation cycle, and the collected value of the latter status monitoring data is the lowest value of the next operation cycle. The high representative data value and the low representative data value are determined according to the state monitoring data with the highest and lowest collected values in the operation cycle.
[0007] Further, the determining of the high representative data value and the low representative data value according to the state monitoring data with the highest and lowest collected values in the operation cycle includes: Obtain the highest and lowest status monitoring data in each operation cycle in nearly m groups of operation cycles, each group of operation cycles includes n consecutive operation cycles, and m and n are both positive integers; Calculate the average values of the highest and lowest collected values of the status monitoring data in each group of operation cycles respectively to obtain the group high representative value and the group low representative value; The high representative data value and the low representative data value are calculated based on all group high representative values and group low representative values.
[0008] Further, the calculating of the high representative data value and the low representative data value according to all group high representative values and group low representative values comprises: Let the group high representative value of the i-th group of operation cycles be , the group low representative value of the i-th group of operation cycles is , high represents the data value , low represents the data value ,but ; ; ; Where a is a preset constant greater than zero. is the difference between the highest and lowest values in the pre-acquired standard data value range, and c is less than zero. The number of
[0009] Further, the determining the operating status result of the device according to the high representative data value, the low representative data value and the pre-acquired standard data value range includes: The result of subtracting the low representative data value from the high representative data value is the representative difference; Calculate the standard representative value according to the difference between the representative difference value and the highest value and the lowest value of the pre-acquired standard data value range, and determine the difference between the high representative data value and the highest value of the pre-acquired standard data value range and the difference between the low representative data value and the lowest value of the pre-acquired standard data value range as the high difference value and the low difference value respectively; Determine the relationship between the standard representative value and the pre-acquired standard representative threshold value, and if the standard representative value is higher than the standard representative threshold value, determine that the operating state of the device is abnormal; If the standard representative value is not higher than the standard representative threshold, determine whether the high gap value and / or the low gap value is higher than the representative gap threshold. If it is judged to be yes, the operating status result of the equipment is judged to be parameter abnormality. If it is judged to be no, the operating status result of the equipment is judged to be normal operation.
[0010] Furthermore, the standard representative value is equal to the ratio of the representative gap to the difference between the highest value and the lowest value of the pre-acquired standard data value range as the representative ratio.
[0011] Further, the standard representative value is equal to the representative gap.
[0012] Further, the determining the operating status result of the device according to the high representative data value, the low representative data value and the pre-acquired standard data value range includes: Let b be equal to a non-zero If the number of , then the running status of the device is judged to be abnormal, where: A preset positive integer.
[0013] In the second aspect, the present application provides a medical equipment operation status analysis system based on the Internet of Things. The system includes a data acquisition terminal and a status analysis platform connected in communication, the data acquisition terminal is used to collect equipment status monitoring data, and the status analysis platform is used to execute any one of the methods described in the first aspect above.
[0014] In summary, this application at least has the following beneficial effects: Provided is a method and system for analyzing the operating status of medical equipment based on the Internet of Things, which can reasonably and accurately determine the operating status results of the equipment, and is conducive to timely discovering the abnormalities and types of abnormalities of the equipment, so as to ensure the standardized and reliable operation of the equipment.
[0015] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein: Figure 1 A block diagram of a medical device operation status analysis system based on the Internet of Things in an embodiment of the present application is shown; Figure 2 A flowchart of a method for analyzing the operating status of medical equipment based on the Internet of Things in an embodiment of the present application is shown. DETAILED DESCRIPTION
[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0018] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.
[0019] The purpose of analyzing the operating status of the equipment is to promptly discover the abnormality of the equipment and to reasonably and accurately determine the abnormal situation of the equipment. For typical medical equipment, the current solution is generally to monitor the key operating parameters of the equipment and judge whether the equipment is abnormal based on the key operating parameters. For example, taking a refrigerator as an example, by monitoring the temperature inside the refrigerator, it is judged whether the refrigerator is abnormal. If the temperature inside the refrigerator obtained by monitoring exceeds the reasonable temperature range, the refrigerator is judged to be abnormal. Although this can also realize the monitoring of equipment abnormalities to a certain extent, on the one hand, individual abnormalities in the parameters of the equipment cannot reflect the abnormality of the equipment, and such a judgment is obviously unreasonable. On the other hand, such a judgment cannot determine the specific situation of the equipment abnormality.
[0020] In order to solve the above problems, the present application provides a method and system for analyzing the operating status of medical equipment based on the Internet of Things, which is conducive to ensuring the standardized and reliable operation of the equipment.
[0021] In a first aspect, the present application provides a medical equipment operation status analysis system based on the Internet of Things.
[0022] The system is applied to the operation status analysis of the equipment, especially to typical medical equipment, such as refrigerators, compressors, etc. Taking the refrigerator as an example, in order to ensure that the temperature inside the refrigerator is within the target temperature range, for example, the target temperature range is 2°C to 8°C, the adjustment principle of the refrigerator includes: when the temperature inside the refrigerator reaches 8°C, the refrigerator refrigerates to reduce the temperature inside the refrigerator to 2°C, and when the temperature inside the refrigerator is cooled to 2°C, the refrigeration is stopped, the refrigerator runs naturally and the internal temperature rises naturally, until the temperature inside the refrigerator rises to 8°C and the refrigeration is started again to reduce the temperature inside the refrigerator to 2°C, so that the temperature inside the refrigerator can be maintained at 2°C-8°C. The system in the embodiment of the present application is applied to such equipment that needs to keep the target parameters within the target parameter range, and analyzes the operation status of the equipment based on the parameters obtained from actual monitoring.
[0023] Figure 1 A block diagram of a medical equipment operation status analysis system based on the Internet of Things in an embodiment of the present application is shown.
[0024] Reference Figure 1 The system includes a data acquisition terminal 110 and a status analysis platform 120 which are communicatively connected.
[0025] The data acquisition terminal 110 may be a target parameter acquisition component (e.g., a temperature detection module of a refrigerator) built into the device to be analyzed (e.g., a refrigerator). Of course, the data acquisition terminal 110 may also be an independently configured acquisition component, which only needs to be able to acquire the state monitoring data of the target parameter of the device to be analyzed. The data acquisition terminal 110 acquires the state monitoring data of the device to be analyzed at a fixed frequency, for example, the refrigerator acquires the temperature every 5 minutes.
[0026] The state analysis platform 120 is used to receive the state monitoring data collected by the data collection terminal 110, and analyze the operation state of the equipment according to the collected state monitoring data to obtain the operation state result. The specific analysis logic of the state analysis platform 120 is introduced in the disclosure of the second aspect of the embodiment of the present application.
[0027] In the second aspect, the present application provides a method for analyzing the operation status of medical equipment based on the Internet of Things. The method can be Figure 1 The status analysis platform 120 in is executed.
[0028] Figure 2 A flowchart of a method for analyzing the operating status of medical equipment based on the Internet of Things in an embodiment of the present application is shown.
[0029] Reference Figure 2 , the method specifically comprises the following steps: S210: Acquire status monitoring data of the device.
[0030] The state monitoring data carries a timestamp and a collection value. Since the state analysis platform 120 is connected to the data collection terminal 110 in communication, the state analysis platform 120 can directly obtain the state monitoring data carrying a timestamp and a collection value collected by the data collection terminal 120. The timestamp reflects the collection time of the state monitoring data, and the collection value reflects the value of the target parameter at the collection time, such as the temperature value.
[0031] S220: Determine a high representative data value and a low representative data value according to the status monitoring data.
[0032] The high representative data value is associated with the highest value of the collected values of the status monitoring data in the operation cycle, and the low representative data value is associated with the lowest value of the collected values of the status monitoring data in the operation cycle.
[0033] The method of this step specifically includes: analyzing the collected values of the status monitoring data based on the timestamp to obtain the status monitoring data with the highest and lowest collected values in each operation cycle; if the collected value of the subsequent status monitoring data in two adjacent status monitoring data is lower than the collected value of the previous status monitoring data, then the collected value of the previous status monitoring data is the highest value of the previous operation cycle, and the collected value of the subsequent status monitoring data is the lowest value of the next operation cycle; and determining the high representative data value and the low representative data value according to the status monitoring data with the highest and lowest collected values of the operation cycle.
[0034] Specifically, the method of determining the high representative data value and the low representative data value based on the status monitoring data of the highest and lowest collected values of the operation cycle includes: obtaining the highest and lowest status monitoring data in each operation cycle in nearly m groups of operation cycles, each group of operation cycles includes n consecutive operation cycles, and m and n are both positive integers; respectively calculating the average values of the highest and lowest collected values of the status monitoring data in each group of operation cycles to obtain the group high representative value and the group low representative value; and calculating the high representative data value and the low representative data value based on all the group high representative values and the group low representative values.
[0035] In the above content, the operation cycle is the change cycle of the target parameter of the device to be analyzed. Taking the normal operation of the refrigerator as an example, the refrigerator naturally rises from 2℃ to 8℃ and the refrigerator cools down the temperature to 2℃ again, which is a cycle. Since the natural heating cycle of the refrigerator is long, the cycle of the refrigerator cooling down the temperature from 8℃ to 2℃ is short (shorter than the collection cycle, for example, the collection cycle is 5min), so the probability that the state monitoring data falls in the refrigerator cooling process is low, and at most only one state monitoring data falls in the refrigerator cooling process in the same cycle. Based on the above, it can be fuzzy determined that the collection value of the state monitoring data collected by the refrigerator increases from the lowest to the highest in one operation cycle, and the collection value of the state monitoring data collected first in each operation cycle is the lowest, and the collection value of the state monitoring data collected last is the highest. Therefore, the edge of the operation cycle can be determined according to the size of the adjacent collection values, that is, if the collection value of the state monitoring data in the latter of the two adjacent state monitoring data is lower than the collection value of the former state monitoring data, it means that the former state monitoring data is the highest value of the previous operation cycle, and the latter state monitoring data is the lowest value of the next operation cycle.
[0036] After determining the highest and lowest status monitoring data of the collected values for each operating cycle, the group high representative value and group low representative value of each group of operating cycles can be determined. The group high representative value is equal to the average value of all single-group highest collected values of a group of operating cycles, and the group low representative value is equal to the average value of all single-group lowest collected values of a group of operating cycles.
[0037] The calculation of the high representative data value and the low representative data value according to all the group high representative values and the group low representative values comprises: assuming that the group high representative value of the i-th group operation cycle is , the group low representative value of the i-th group of operation cycles is , high represents the data value , low represents the data value ,but ; ; ; Where a is a preset constant greater than zero. is the difference between the highest and lowest values in the pre-acquired standard data value range, and c is less than zero. The number of a and The value of is determined according to data experience training. Based on the above content, the high representative value and the low representative value of the group can be calculated and determined.
[0038] S230: Determine the operating status result of the device according to the high representative data value, the low representative data value and the pre-acquired standard data value range.
[0039] The method of this step specifically includes: calculating the result of subtracting the low representative data value from the high representative data value as a representative difference; calculating the standard representative value according to the difference between the representative difference and the highest value and the lowest value of the pre-acquired standard data value range, and determining that the difference between the high representative data value and the highest value of the pre-acquired standard data value range and the difference between the low representative data value and the lowest value of the pre-acquired standard data value range are the high difference value and the low difference value respectively; judging the relationship between the standard representative value and the pre-acquired standard representative threshold value, if the standard representative value is higher than the standard representative threshold value, judging that the operating status result of the device is abnormal; if the standard representative value is not higher than the standard representative threshold value, judging whether the high difference value and / or the low difference value are higher than the representative difference threshold value, if it is judged to be yes, judging that the operating status result of the device is abnormal parameter, and if it is judged to be no, judging that the operating status result of the device is normal operation.
[0040] In another example, the standard representative value is equal to the representative gap.
[0041] The logical principle of the above method is: if the difference between the high representative data value and the low representative data value is too large or too small, it means that the difference between the group interval between the actual target parameter range of the device (the data range between the high representative data value and the low representative data value) and the ideal target parameter range is too large, and the device has been adjusted out of balance, that is, the device is judged to be abnormal and needs to be repaired and maintained. If the difference between the group interval between the actual target parameter range of the device and the original target parameter range is not large, it means that the device is at least adjusted and balanced in the target parameter range. At this time, it is necessary to further judge the relationship between the high representative data value and the low representative data value of the device and the upper and lower limits of the ideal target parameter range.
[0042] In the case of non-device abnormality, if both the high representative data value and the low representative data value are high or low (higher than the upper and lower limits of the ideal target parameter range by a certain gap), it means that the parameter adjustment power (for example, the refrigerator's cooling power) is set unreasonably due to factors such as equipment aging. At this time, the device is judged to have parameter abnormality, and the device can be adjusted according to the relationship between the ideal target parameter range and the actual target parameter range. For example, if the upper and lower limits of the actual target temperature range of the refrigerator are higher than the upper and lower limits of the ideal target temperature range, the refrigerator's cooling power is increased. If both the high representative data value and the low representative data value are normal, it means that the device is operating normally.
[0043] In one example, the standard representative value is equal to the ratio of the representative gap to the difference between the highest value and the lowest value of the pre-acquired standard data value range as the representative ratio.
[0044] In addition, the method of this step also includes: setting b equal to a non-zero The number of , then the running status of the device is judged to be abnormal, where: is a preset positive integer. The actual meaning of its feedback is that when analyzing the gap between the group high representative value and the group low representative value of each group of operation cycles, it is found that there are too many abnormal groups (the gap between the group high representative value and the group low representative value deviates too much from the ideal gap). At this time, the operation status of the equipment should be directly judged as abnormal equipment.
[0045] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.
[0046] In summary, this application at least has the following beneficial effects: Provided is a method and system for analyzing the operating status of medical equipment based on the Internet of Things, which can reasonably and accurately determine the operating status results of the equipment, and is conducive to timely discovering the abnormalities and types of abnormalities of the equipment, so as to ensure the standardized and reliable operation of the equipment.
[0047] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.
Claims
1. A method for analyzing the operation status of medical equipment based on the Internet of Things, characterized in that: include: Acquire status monitoring data of the device, wherein the status monitoring data carries a timestamp and a collection value; Determine a high representative data value and a low representative data value according to the state monitoring data, wherein the high representative data value is associated with the highest value of the collected values of the state monitoring data in the operation cycle, and the low representative data value is associated with the lowest value of the collected values of the state monitoring data in the operation cycle; The operating status result of the device is determined according to the high representative data value, the low representative data value and the pre-acquired standard data value range.
2. The method according to claim 1, characterized in that: Determining a high representative data value and a low representative data value according to the state monitoring data comprises: Analyze the collected values of the status monitoring data based on the timestamp to obtain the highest and lowest collected values of each operation cycle. If the collected value of the latter status monitoring data in two adjacent status monitoring data is lower than the collected value of the former status monitoring data, the collected value of the former status monitoring data is the highest value of the previous operation cycle, and the collected value of the latter status monitoring data is the lowest value of the next operation cycle. The high representative data value and the low representative data value are determined according to the state monitoring data with the highest and lowest collected values in the operation cycle.
3. The method according to claim 2, characterized in that Determining the high representative data value and the low representative data value according to the state monitoring data with the highest and lowest collected values in the operation cycle includes: Obtain the highest and lowest status monitoring data in each operation cycle in nearly m groups of operation cycles, each group of operation cycles includes n consecutive operation cycles, and m and n are both positive integers; Calculate the average values of the highest and lowest collected values of the status monitoring data in each group of operation cycles respectively to obtain the group high representative value and the group low representative value; The high representative data value and the low representative data value are calculated based on all group high representative values and group low representative values.
4. The method according to claim 3, characterized in that The calculating of the high representative data value and the low representative data value according to all the group high representative values and the group low representative values comprises: Let the group high representative value of the i-th group of operation cycles be , the group low representative value of the i-th group of operation cycles is , high represents the data value , low represents the data value ,but ； Where a is a preset constant greater than zero. is the difference between the highest and lowest values in the pre-acquired standard data value range, and c is less than zero. The number of 5. The method according to any one of claims 1 to 4, characterized in that: The determining of the operating status result of the device according to the high representative data value, the low representative data value and the pre-acquired standard data value range includes: The result of subtracting the low representative data value from the high representative data value is the representative difference; Calculate the standard representative value according to the difference between the representative difference value and the highest value and the lowest value of the pre-acquired standard data value range, and determine the difference between the high representative data value and the highest value of the pre-acquired standard data value range and the difference between the low representative data value and the lowest value of the pre-acquired standard data value range as the high difference value and the low difference value respectively; Determine the relationship between the standard representative value and the pre-acquired standard representative threshold value, and if the standard representative value is higher than the standard representative threshold value, determine that the operating state of the device is abnormal; If the standard representative value is not higher than the standard representative threshold, determine whether the high gap value and / or the low gap value is higher than the representative gap threshold. If it is determined to be yes, the operating status result of the equipment is determined to be parameter abnormality. If it is determined to be no, the operating status result of the equipment is determined to be normal operation.
6. The method according to claim 5, characterized in that The standard representative value is equal to the ratio of the representative gap to the difference between the highest value and the lowest value of the pre-acquired standard data value range as the representative ratio.
7. The method according to claim 5, characterized in that The standard representative value is equal to the representative gap.
8. The method according to claim 4, characterized in that The determining of the operating status result of the device according to the high representative data value, the low representative data value and the pre-acquired standard data value range includes: Let b be equal to a non-zero If the number of , then the running status of the device is judged to be abnormal, where: A preset positive integer.
9. A medical equipment operation status analysis system based on the Internet of Things, characterized in that: It comprises a data acquisition terminal (110) and a status analysis platform (120) which are communicatively connected, wherein the data acquisition terminal (110) is used to acquire status monitoring data of the equipment, and the status analysis platform (120) is used to execute the method according to any one of claims 1 to 8.