Medical equipment operation and maintenance management system based on Internet of Things

Abstract

The invention relates to the technical field of medical equipment operation and maintenance, and particularly discloses a medical equipment operation and maintenance management system based on the Internet of Things, and the system comprises a data collection module which is used for collecting the real-time operation state data of medical equipment in the starting and standby states of the current day, and uploading the real-time operation state data to a cloud end, the real-time operation state data of the medical equipment in the standby state of the current day are combined for analysis, the operation state of the medical equipment can be preliminarily judged, and the operation risk value of the medical equipment in the standby state is calculated and obtained in the subsequent operation process in combination with the operation state data of the medical equipment in the previous working process and is analyzed; according to the method, the operation state of the medical equipment can be further judged, and the reliability of a calculation result can be improved by combining diversified data, so that the sensitivity of judging the operation state of the medical equipment is improved, the accuracy and reliability of a judgment result are improved, preventive maintenance can be realized, and the practicability is high. And equipment faults during follow-up work are avoided.

Description

Technical Field

[0001] The present invention relates to the field of medical equipment operation and maintenance technology, and in particular to a medical equipment operation and maintenance management system based on the Internet of Things. Background Art

[0002] Medical equipment refers to instruments, equipment, appliances, materials or other items used on the human body alone or in combination, and also includes the required software. Medical equipment is the basic condition for continuously improving the level of medical science and technology, and is also an important symbol of the degree of modernization. Medical equipment has become an important field of modern medicine.

[0003] As an indispensable item in modern medicine, the importance of medical equipment is self-evident. Therefore, in order to ensure the normal use of medical equipment, a medical equipment operation and maintenance management system is usually used to comprehensively monitor medical equipment using information technology and management methods. Through real-time monitoring of medical equipment throughout the day, equipment downtime can be reduced to improve the efficiency, reliability and safety of medical equipment use, and the time and cost of manual inspections and troubleshooting can be reduced, thereby improving the quality and efficiency of medical services.

[0004] Common medical equipment operation and maintenance management systems determine the operating status of medical equipment through real-time monitoring of medical equipment. However, when medical equipment is not in use, it is usually stored in a standby state. When the medical equipment is in a standby state, changes in its operating status are difficult to monitor, so it is impossible to predict the status of the medical equipment during subsequent work, and thus preventive maintenance cannot be achieved, which may lead to equipment failure during subsequent work. Summary of the invention

[0005] The purpose of the present invention is to provide a medical equipment operation and maintenance management system based on the Internet of Things to solve the following technical problems:

[0006] How to make accurate judgments on the operating status of medical equipment to achieve preventive maintenance.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A medical equipment operation and maintenance management system based on the Internet of Things, the system comprising:

[0009] The data collection module is used to collect the real-time operating status data of the medical equipment in the power-on and standby states of the day and upload it to the cloud;

[0010] The data recording module is used to record the operating status data of the medical equipment during its past operation and upload it to the cloud;

[0011] The data processing and analysis module is used to calculate the operating state influence coefficient of the medical device in the standby state according to the information collected by the data collection module, and calculate the operating risk value of the medical device in the standby state in combination with the data recorded by the data recording module, and analyze the two sets of data respectively, and judge the level of the operating risk of the medical device;

[0012] The early warning module is used to issue an early warning to remind staff to operate and maintain the medical equipment in a timely manner when the data processing and analysis module determines that the operation risk of the medical equipment is high.

[0013] Furthermore, the analysis process of the data processing and analysis module includes:

[0014] S1: The real-time operating status data of the medical device in the standby state is collected by the data acquisition module, and the operating status influence coefficient of the medical device at different time points in the standby state is calculated;

[0015] S2: Analyze the preset operating state influence coefficient threshold of the medical device in the standby state, and determine whether there is a risk in the operating state of the medical device at different time points in the standby state according to the analysis result. If yes, issue an early warning, otherwise, proceed to step S3;

[0016] S3: By combining the operation status data of the medical equipment in the past and the startup data of the medical equipment on the day, the operation risk value of the medical equipment at different time points in the standby state is calculated;

[0017] S4: Perform analysis based on the preset operating risk value threshold of the medical device in the standby state, and further determine whether there is a risk in the operating state of the medical device at different time points in the standby state based on the analysis results. If yes, issue a warning, otherwise, do not issue a warning.

[0018] Furthermore, the calculation process in S1 includes:

[0019] By formula Calculate the operating status influence coefficient ρ of the medical equipment at the tth time point in the standby state t ;

[0020] Where t is the data collection time point at a fixed time interval when the medical device is in standby mode, pj t is the power consumption of the medical device at the tth time point in standby mode, pj y is the preset power consumption, gh t is the power of the medical device at the tth time point in standby mode, gh y is the preset power, is the indicator light status influence coefficient, which is set based on experience fitting, dyt is the voltage value of the medical device at the tth time point in standby mode, dy y is the preset standby voltage value, dy b For t The standard value of zs t is the cooling fan speed of the medical device at the tth time point in standby mode, zs y The preset cooling fan speed of the medical device in standby mode, zs b For zs t The standard value of f k To define a function, when f k When (x)>1, let f k (x) = (x), otherwise, let f k (x)=1.

[0021] Furthermore, the judgment process in S2 includes:

[0022] By calculating the operating status influence coefficient ρ of the medical equipment at each time point in the standby state t The threshold value of the operating state influence coefficient ρ of the medical equipment in the standby state is respectively w Make a comparison;

[0023] If all ρ t Both are less than ρ w ,The system determines that there is no risk in the operation status of the medical device in the standby state, and the possibility of risk in subsequent use is small, and further analyzes it in combination with the data of the data recording module;

[0024] If any ρ t Greater than ρ w ,The system determines that there is a risk in the operating status of the medical equipment in standby mode at this point in time, and there is a high possibility of risk in subsequent use, and promptly issues an early warning through the early warning module.

[0025] Furthermore, the calculation process in S3 includes:

[0026] By formula Calculate and obtain the operating status influence coefficient θ of the medical equipment during operation;

[0027] Where i is any operation of the medical device in the past period of time, n is the total number of operations of the medical device in the past period of time, hd i is the total power consumption of the medical device during the i-th operation in the past period of time, sc i is the total number of minutes of the i-th working time of the medical device in the past period of time, a is a data collection at a fixed time interval, b is the total number of data collection times, and dy aiis the voltage intensity of the medical device at the ath time point during the i-th operation in the past period of time, For all ai The average value, fz y is the preset average power consumption per minute, cs i is the temperature of the medical device during the i-th operation in the past period of time, cs y is the preset temperature, cs b For cs i The standard value of .

[0028] Furthermore, the calculation process in S3 also includes:

[0029] By formula Calculate the operating risk value γ of the medical equipment at the tth time point in the standby state t ;

[0030] Among them, kjsj is the startup time of the medical equipment on that day, kjsj y is the preset startup time, nh is the startup energy consumption of the medical equipment on that day, nh y Preset power-on energy consumption for medical equipment, nh b is the standard value of nh, f r It is the adjustment coefficient comparison table function, because according to the empirical data The influence of the range of numerical values ​​on the operational risk value is obtained based on the test data, and x1 and x2 are weight coefficients.

[0031] Furthermore, the judgment process in S4 includes:

[0032] By calculating the operating risk value γ of the medical equipment at the tth time point in the standby state t Compared with the preset operation risk value threshold γ w Make a comparison;

[0033] If all γ t Both are less than γ w , the system determines that there is no risk in the operating state of the medical device in the standby state, and further determines that the possibility of risk occurring in the subsequent work process is small;

[0034] If any γ t Greater than γ w ,The system determines that there is a risk in the operating status of the medical equipment in standby mode at that time point, and further determines that there is a high possibility of risk in the subsequent work process, and finally issues a warning in time through the early warning module.

[0035] Furthermore, the data collected by the data collection module includes:

[0036] Real-time operating status data of medical equipment in standby mode on the day, including power consumption, power, voltage and cooling fan speed of medical equipment in standby mode;

[0037] The medical equipment startup data for the day, including the startup time and startup energy consumption of the medical equipment for the day;

[0038] The information recorded by the data recording module includes: the total power consumption, total minutes, average power consumption, temperature and voltage intensity of the medical device during operation over a period of time in the past.

[0039] Beneficial effects of the present invention:

[0040] (1) The present invention can make a preliminary judgment on the operating status of the medical device by analyzing the real-time operating status data of the medical device in the standby state on the same day, and then calculate and analyze the operating risk value of the medical device in the standby state in combination with the operating status data of the medical device in the past work, so as to further judge the operating status of the medical device, and combine with diversified data to improve the reliability of the calculation results, thereby improving the sensitivity of judging the operating status of the medical device, improving the accuracy and reliability of the judgment results, and realizing preventive maintenance to avoid equipment failure in subsequent work.

[0041] (2) The present invention uses diversified information such as real-time operating status data of medical equipment in standby mode, operating status data of medical equipment in past operation and startup data of medical equipment on the same day as data support to judge the operating status of medical equipment. The reliability and accuracy of the present invention are high, thereby avoiding the situation where the status of the medical equipment in subsequent operation cannot be predicted due to the difficulty in monitoring the changes in the operating status of the medical equipment in standby mode, thereby achieving preventive maintenance.

[0042] (3) The present invention calculates the operating state influence coefficient ρ of the medical device at each time point in the standby state t The threshold value of the operating state influence coefficient ρ of the medical equipment in the standby state is respectively w Through this comparison method, a preliminary judgment can be made on whether there is a risk in the operating status of the medical equipment in standby mode. Then, when it is judged that there is no risk, further analysis is performed in combination with the data of the data recording module. When it is judged that there is a risk, an early warning is issued in time through the early warning module. Through such a setting, the status of the medical equipment in subsequent work can be predicted, thereby achieving preventive maintenance.

[0043] (4) The present invention calculates the operating risk value γ of the medical device at the tth time point in the standby state t Compared with the preset operation risk value threshold γ wBy making a comparison, we can make an accurate judgment on whether there is an abnormal risk in the operating status of the medical equipment in the standby state, and further predict the possibility of risks in the subsequent work process. When it is judged that the possibility of risks in the subsequent work process is high, an early warning module can be used to issue an early warning in time. Through such a setting, diversified data can improve the monitoring effect of the operating status of medical equipment in standby state, thereby performing preventive maintenance on medical equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The present invention will be further described below in conjunction with the accompanying drawings.

[0045] Figure 1 It is a schematic block diagram of a medical equipment operation and maintenance management system based on the Internet of Things in the present invention;

[0046] Figure 2 It is a flow chart of the analysis process of the data processing and analysis module in the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] See also Figure 1 As shown, in one embodiment, the present application provides a medical equipment operation and maintenance management system based on the Internet of Things, the system comprising:

[0049] The data collection module is used to collect the real-time operating status data of the medical equipment in the power-on and standby states of the day and upload it to the cloud;

[0050] The data recording module is used to record the operating status data of the medical equipment during its past operation and upload it to the cloud;

[0051] The data processing and analysis module is used to calculate the operating state influence coefficient of the medical device in the standby state according to the information collected by the data collection module, and calculate the operating risk value of the medical device in the standby state in combination with the data recorded by the data recording module, and analyze the two sets of data respectively, and judge the level of the operating risk of the medical device;

[0052] The early warning module is used to issue an early warning to remind the staff to operate and maintain the medical equipment in a timely manner when the data processing and analysis module determines that the operation risk of the medical equipment is high;

[0053] Through the above technical solution, this embodiment provides a data acquisition module and a data recording module, which are respectively used to collect the real-time operating status data of the medical device in the startup and standby states of the day, and record the operating status data of the medical device in the past work, and upload them to the cloud. When the medical device is in use, the data processing and analysis module can calculate the operating status influence coefficient of the medical device in the standby state according to the information collected by the data acquisition module, and calculate the operating risk value of the medical device in the standby state in combination with the data recorded by the data recording module, and analyze the two sets of data respectively, and judge the level of the operating risk of the medical device;

[0054] Through such a setting, when monitoring medical equipment, the system can make a preliminary judgment on the operating status of the medical equipment by analyzing the real-time operating status data of the medical equipment in the standby state on that day, and subsequently calculate and analyze the operating risk value of the medical equipment in the standby state in combination with the operating status data of the medical equipment in the past work, so as to further judge the operating status of the medical equipment, and combine with diversified data to improve the reliability of the calculation results, thereby improving the sensitivity of judging the operating status of the medical equipment, improving the accuracy and reliability of the judgment results, and realizing preventive maintenance to avoid equipment failure in subsequent work.

[0055] See also Figure 2 As shown, the analysis process of the data processing and analysis module includes:

[0056] S1: The real-time operating status data of the medical device in the standby state is collected by the data acquisition module, and the operating status influence coefficient of the medical device at different time points in the standby state is calculated;

[0057] S2: Analyze the preset operating state influence coefficient threshold of the medical device in the standby state, and determine whether there is a risk in the operating state of the medical device at different time points in the standby state according to the analysis result. If yes, issue an early warning, otherwise, proceed to step S3;

[0058] S3: By combining the operation status data of the medical equipment in the past and the startup data of the medical equipment on the day, the operation risk value of the medical equipment at different time points in the standby state is calculated;

[0059] S4: Analyze the preset operation risk value threshold of the medical device in the standby state, and further judge whether there is a risk in the operation state of the medical device at different time points in the standby state according to the analysis result. If yes, issue a warning, otherwise, do not issue a warning;

[0060] Through the above technical solution, this embodiment provides an analysis process of the data processing and analysis module. Through this analysis method, not only can the operation status of the medical device be preliminarily judged according to the operation status influence coefficient of the medical device at different time points in the standby state, but also the operation status of the medical device can be secondary judged in combination with the operation risk value of the medical device at different time points in the standby state;

[0061] Furthermore, since the calculation result is based on diversified information such as the real-time operating status data of the medical device in standby mode, the operating status data of the medical device in past operation, and the startup data of the medical device on the same day as data support, its reliability and accuracy are relatively high, thereby avoiding the situation where the changes in the operating status of the medical device in standby mode are difficult to monitor, resulting in the inability to predict the status of the medical device in subsequent operation, thereby achieving preventive maintenance.

[0062] The calculation process in S1 includes:

[0063] By formula Calculate the operating status influence coefficient ρ of the medical equipment at the tth time point in the standby state t ;

[0064] Where t is the data collection time point at a fixed time interval when the medical device is in standby mode, pj t is the power consumption of the medical device at the tth time point in standby mode, pj y is the preset power consumption, gh t is the power of the medical device at the tth time point in standby mode, gh y is the preset power, is the indicator light status influence coefficient, which is set based on experience fitting, dy t is the voltage value of the medical device at the tth time point in standby mode, dy y is the preset standby voltage value, dy b For t The standard value of zs can be selected and set according to the allowable error in the empirical data. t is the cooling fan speed of the medical device at the tth time point in standby mode, zs y The preset cooling fan speed of the medical device in standby mode, zs b For zs t The standard value of f can be selected and set according to the allowable error in empirical data. k To define a function, when f k When (x)>1, let f k (x) = (x), otherwise, let f k (x) = 1;

[0065] Through the above technical solution, this embodiment provides the operating state influence coefficient ρ of the medical device at the tth time point in the standby state t , can be obtained by formula It is calculated that when the medical device is in standby mode, the higher the power consumption and power consumption, the higher the power consumption. The abnormal increase in power consumption may mean that there is leakage, component aging or improper setting inside the device. The greater the difference between the voltage value and the preset standby voltage value, the more unstable the voltage is. Unstable voltage may cause the power adapter of the electrical device to overload or overheat, thus shortening its service life or directly damaging it. Finally, abnormal fan speed may mean fan failure, dust accumulation or cooling system problems.

[0066] Therefore, when the power consumption and power of the medical device at the tth time point in standby mode are higher, the speed of the cooling fan is higher and the difference between the voltage value and the preset standby voltage value is larger, then the impact coefficient of the operating status of the medical device at the tth time point in standby mode is larger; conversely, when the power consumption and power of the medical device at the tth time point in standby mode are lower, the speed of the cooling fan is higher and the difference between the voltage value and the preset standby voltage value is smaller, then the impact coefficient of the operating status of the medical device at the tth time point in standby mode is smaller. Through this calculation method, the operating status of the medical device at different time points in the standby process can be reflected through data, thereby providing reliable information data for the subsequent preliminary judgment on whether there is any abnormality in its operating status, thereby improving the accuracy and reliability of the judgment results.

[0067] The judgment process in S2 includes:

[0068] By calculating the operating status influence coefficient ρ of the medical equipment at each time point in the standby state t The threshold value of the operating state influence coefficient ρ of the medical equipment in the standby state is respectively w Make a comparison;

[0069] If all ρ t Both are less than ρ w ,The system determines that there is no risk in the operation status of the medical device in the standby state, and the possibility of risk in subsequent use is small, and further analyzes it in combination with the data of the data recording module;

[0070] If any ρ t Greater than ρ w , the system determines that the operating status of the medical device in standby state at that time point is at risk, and there is a high possibility of risk in subsequent use, and promptly issues an early warning through the early warning module;

[0071] Through the above technical solution, this embodiment calculates the operating state influence coefficient ρ of the medical device at each time point in the standby state t The threshold value of the operating state influence coefficient ρ of the medical equipment in the standby state is respectively w Through this comparison method, a preliminary judgment can be made on whether there is a risk in the operating status of the medical equipment in standby mode. Then, when it is judged that there is no risk, further analysis is performed in combination with the data of the data recording module. When it is judged that there is a risk, an early warning is issued in time through the early warning module. Through such a setting, the status of the medical equipment in subsequent work can be predicted, thereby achieving preventive maintenance.

[0072] The calculation process in S3 includes:

[0073] By formula Calculate and obtain the operating status influence coefficient θ of the medical equipment during operation;

[0074] Where i is any operation of the medical device in the past period of time, n is the total number of operations of the medical device in the past period of time, hd i is the total power consumption of the medical device during the i-th operation in the past period of time, sc i is the total number of minutes of the i-th working time of the medical device in the past period of time, a is a data collection at a fixed time interval, b is the total number of data collection times, and dy ai is the voltage intensity of the medical device at the ath time point during the i-th operation in the past period of time, For all ai The average value, fz y is the preset average power consumption per minute, cs i is the temperature of the medical device during the i-th operation in the past period of time, cs y is the preset temperature, cs b For cs i The standard value of the above standard value can be selected and set according to the allowable error in the empirical data;

[0075] Through the above technical solution, this embodiment provides the operating state influence coefficient θ of the medical device during operation, which can be calculated by the formula Calculated, where the formula The coefficient of dispersion of the voltage value of the medical device when it worked for the i-th time in the past period of time can be calculated. Obviously, when the coefficient of dispersion of the total power consumption, temperature and voltage value of the medical device when it worked for the i-th time in the past period of time is higher, and the total number of minutes of the medical device working for the i-th time in the past period of time is fewer, then the influence coefficient of the operation state of the medical device during the working process is greater. Conversely, when the coefficient of dispersion of the total power consumption, temperature and voltage value of the medical device when it worked for the i-th time in the past period of time is lower, and the total number of minutes of the medical device working for the i-th time in the past period of time is higher, then the influence coefficient of the operation state of the medical device during the working process is smaller. Through this calculation method, the operation state of the medical device during the working process can be reflected, thereby providing reliable and accurate data support for the subsequent calculation of the operation risk value of the medical device at the t-th time point in the standby state, and then the accuracy of the calculation result, as well as the accuracy and reliability of the subsequent secondary judgment results.

[0076] The calculation process in S3 also includes:

[0077] By formula Calculate the operating risk value γ of the medical equipment at the tth time point in the standby state t ;

[0078] Among them, kjsj is the startup time of the medical equipment on that day, kjsj y is the preset startup time, nh is the startup energy consumption of the medical equipment on that day, nh y Preset power-on energy consumption for medical equipment, nh b is the standard value of nh. The above standard value can be selected and set according to the allowable error in the empirical data. r It is the adjustment coefficient comparison table function, because according to the empirical data The influence of the range of values ​​on the operational risk value is obtained based on the test data, and x1 and x2 are weight coefficients, which can be set based on empirical fitting;

[0079] Through the above technical solution, this embodiment provides the operation risk value of the medical device at the tth time point in the standby state, which can be calculated by the formula The calculation is obtained by combining the operating status influence coefficient of the medical equipment at the tth time point in the standby state with the operating status influence coefficient of the medical equipment during the working process, and extracting the start-up time and start-up energy consumption of the medical equipment on that day. Multi-dimensional data support can improve the accuracy and reliability of the calculation results, thereby providing strong data support for whether there is a risk in the subsequent operating status of the medical equipment in the standby state, and ensuring the accuracy of the judgment results.

[0080] The judgment process in S4 includes:

[0081] By calculating the operating risk value γ of the medical equipment at the tth time point in the standby state t Compared with the preset operation risk value threshold γ w Make a comparison;

[0082] If all γ t Both are less than γ w , the system determines that there is no risk in the operating state of the medical device in the standby state, and further determines that the possibility of risk occurring in the subsequent work process is small;

[0083] If any γ t Greater than γ w ,The system determines that the operating status of the medical equipment in standby state at that time point has risks, and further determines that the possibility of risks occurring in the subsequent work process is high, and finally issues a warning in time through the warning module;

[0084] Through the above technical solution, this embodiment calculates the operating risk value γ of the medical device at the tth time point in the standby state t Compared with the preset operation risk value threshold γ w By making a comparison, we can make an accurate judgment on whether there is an abnormal risk in the operating status of the medical equipment in the standby state, and further predict the possibility of risks in the subsequent work process. When it is judged that the possibility of risks in the subsequent work process is high, an early warning module can be used to issue an early warning in time. Through such a setting, diversified data can improve the monitoring effect of the operating status of medical equipment in standby state, thereby performing preventive maintenance on medical equipment.

[0085] The data collected by the data collection module includes:

[0086] Real-time operating status data of medical equipment in standby mode on the day, including power consumption, power, voltage and cooling fan speed of medical equipment in standby mode;

[0087] The medical equipment startup data for the day, including the startup time and startup energy consumption of the medical equipment for the day;

[0088] The information recorded by the data recording module includes: the total power consumption, total minutes, average power consumption, temperature and voltage intensity of the medical device during operation over the past period of time;

[0089] Through the above technical scheme, this embodiment provides data collected by the data acquisition module and data recorded by the data recording module. By accurately obtaining diversified and multi-dimensional information data, the accuracy of the calculation results of two sets of data, namely the operating status influence coefficient of the medical device at the tth time point in the standby state and the operating risk value of the medical device at the tth time point in the standby state, can be guaranteed, thereby improving the accuracy and reliability of the judgment of whether there is an abnormal risk in the operating status of the medical device, and further providing a monitoring effect of the operating status of the medical device in the standby state.

[0090] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A medical equipment operation and maintenance management system based on the Internet of Things, characterized in that: The system comprises: The data collection module is used to collect the real-time operating status data of the medical equipment in the power-on and standby states of the day and upload it to the cloud; The data recording module is used to record the operating status data of the medical equipment during its past operation and upload it to the cloud; The data processing and analysis module is used to calculate the operating state influence coefficient of the medical device in the standby state according to the information collected by the data collection module, and calculate the operating risk value of the medical device in the standby state in combination with the data recorded by the data recording module, and analyze the two sets of data respectively, and judge the level of the operating risk of the medical device; The early warning module is used to issue an early warning to remind staff to operate and maintain the medical equipment in a timely manner when the data processing and analysis module determines that the operation risk of the medical equipment is high.

2. According to the medical equipment operation and maintenance management system based on the Internet of Things according to claim 1, it is characterized in that: The analysis process of the data processing and analysis module includes: S1: The real-time operating status data of the medical device in the standby state is collected by the data acquisition module, and the operating status influence coefficient of the medical device at different time points in the standby state is calculated; S2: Analyze the preset operating state influence coefficient threshold of the medical device in the standby state, and determine whether there is a risk in the operating state of the medical device at different time points in the standby state according to the analysis result. If yes, issue an early warning, otherwise, proceed to step S3; S3: By combining the operation status data of the medical equipment in the past and the startup data of the medical equipment on the day, the operation risk value of the medical equipment at different time points in the standby state is calculated; S4: Perform analysis based on the preset operating risk value threshold of the medical device in the standby state, and further determine whether there is a risk in the operating state of the medical device at different time points in the standby state based on the analysis results. If yes, issue a warning, otherwise, do not issue a warning.

3. According to the Internet of Things-based medical equipment operation and maintenance management system of claim 2, it is characterized in that: The calculation process in S1 includes: By formula Calculate and obtain the operating state influence coefficient ρt of the medical equipment at the tth time point in the standby state; Where t is the data collection time point at a fixed time interval when the medical device is in standby mode, pj t is the power consumption of the medical device at the tth time point in standby mode, pj y is the preset power consumption, gh t is the power of the medical device at the tth time point in standby mode, gh y is the preset power, is the indicator light status influence coefficient, which is set based on experience fitting, dy t is the voltage value of the medical device at the tth time point in standby mode, dy y is the preset standby voltage value, dy b For t The standard value of zs t is the cooling fan speed of the medical device at the tth time point in standby mode, zs y The preset cooling fan speed of the medical device in standby mode, zs b For zs t The standard value, f k To define a function, when f k When (x)>1, let f k (x) = (x), otherwise, let f k (x)=1.

4. According to the Internet of Things-based medical equipment operation and maintenance management system according to claim 3, it is characterized in that: The judgment process in S2 includes: By calculating the operating status influence coefficient ρ of the medical equipment at each time point in the standby state t The threshold value of the operating state influence coefficient ρ of the medical equipment in the standby state is respectively w Make a comparison; If all ρ t Both are less than ρ w ,The system determines that there is no risk in the operation status of the medical device in the standby state, and the possibility of risk in subsequent use is small, and further analyzes it in combination with the data of the data recording module; If any ρ t Greater than ρ w ,The system determines that there is a risk in the operating status of the medical equipment in standby mode at this point in time, and there is a high possibility of risk in subsequent use, and promptly issues an early warning through the early warning module.

5. According to the Internet of Things-based medical equipment operation and maintenance management system according to claim 4, it is characterized in that: The calculation process in S3 includes: By formula Calculate and obtain the operating status influence coefficient θ of the medical equipment during operation; Where i is any operation of the medical device in the past period of time, n is the total number of operations of the medical device in the past period of time, hd i is the total power consumption of the medical device during the i-th operation in the past period of time, sc i is the total number of minutes of the i-th working time of the medical device in the past period of time, a is a data collection at a fixed time interval, b is the total number of data collection times, dya i is the voltage intensity of the medical device at the ath time point during the i-th operation in the past period of time, is the average value of all dyai, fz y is the preset average power consumption per minute, cs i is the temperature of the medical device during the i-th operation in the past period of time, cs y is the preset temperature, cs b For cs i The standard value of .

6. The medical equipment operation and maintenance management system based on the Internet of Things according to claim 5 is characterized in that: The calculation process in S3 also includes: By formula Calculate and obtain the operation risk value γt of the medical equipment at the tth time point in the standby state; Among them, kjsj is the startup time of the medical equipment on that day, kjsj y is the preset startup time, nh is the startup energy consumption of the medical equipment on that day, nh y Preset power-on energy consumption for medical equipment, nh b is the standard value of nh, f r It is the adjustment coefficient comparison table function, because according to the empirical data The influence of the numerical range on the operational risk value is obtained based on the test data, and x1 and x2 are weight coefficients.

7. The medical equipment operation and maintenance management system based on the Internet of Things according to claim 6 is characterized in that: The judgment process in S4 includes: By comparing the operation risk value γt of the medical device at the tth time point in the standby state with the preset operation risk value threshold γ w Make a comparison; If all γ t Both are less than γ w , the system determines that there is no risk in the operating state of the medical device in the standby state, and further determines that the possibility of risk occurring in the subsequent work process is small; If any γ t Greater than γ w ,The system determines that there is a risk in the operating status of the medical equipment in standby mode at that time point, and further determines that there is a high possibility of risk in the subsequent work process, and finally issues a warning in time through the early warning module.

8. The medical equipment operation and maintenance management system based on the Internet of Things according to claim 1 is characterized in that: The data collected by the data collection module includes: Real-time operating status data of medical equipment in standby mode on the day, including power consumption, power, voltage and cooling fan speed of medical equipment in standby mode; The startup data of the medical equipment on that day, including the startup time and startup energy consumption of the medical equipment on that day; The information recorded by the data recording module includes: the total power consumption, total minutes, average power consumption, temperature and voltage intensity of the medical device during its operation over a period of time in the past.

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