A smart hospital self-service equipment monitoring and repair method based on the Internet of Things

Abstract

The application discloses a kind of self-service equipment monitoring repair methods of wisdom hospital based on Internet of Things, and relates to Internet of Things technical field.The self-service equipment monitoring repair methods of wisdom hospital based on Internet of Things, based on the real-time monitoring information of each self-service equipment in target hospital, running basic information and extreme weather data analysis of the region where target hospital is located, obtain the fault monitoring early warning value of each self-service equipment in target hospital, judge the fault condition of each self-service equipment in target hospital;Based on the fault monitoring early warning value of each self-service equipment in target hospital, obtain the repair order of self-service equipment needing repair in target hospital;Based on repair order, the maintenance process of self-service equipment is tracked and recorded, and maintenance result is obtained;Whether the repair is qualified is judged by analyzing maintenance result, and secondary repair maintenance is carried out for unqualified repair condition;Real-time monitoring of various self-service equipment in hospital is realized, self-service equipment failure is found in time and repaired, and hospital operation cost and risk are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a method for monitoring and repairing self-service equipment in a smart hospital based on the Internet of Things. Background Art

[0002] Hospitals have a large number of self-service devices, with a wide variety of types, making maintenance and management challenging. Extreme weather conditions can impact the operation and performance of these devices, making the management and maintenance of these devices increasingly important. Traditional maintenance methods can lead to wasted resources and inefficiencies. With the development of information technology, more and more medical institutions are adopting data-driven management approaches, optimizing the maintenance and management of self-service devices through real-time monitoring and data analysis.

[0003] For example, the Chinese invention patent with announcement number CN112445601B discloses an IoT integrated management platform and management method based on a smart brain. A preset number of servers are set up, the servers are associated, and management functions are set for the servers; the servers are connected to the corresponding IoT devices so that the servers receive data transmitted by the corresponding IoT devices while controlling the IoT devices; the communication status data of multiple IoT devices and the device operation data of multiple IoT devices are obtained, and the communication status data corresponds to the first data list and the device operation data corresponds to the second data list; the first data list and the second data list are imported into the standard database, and the imported data is used for comprehensive management, thereby realizing a global analysis of the entire smart park, community, and building, and ensuring the normal operation of the entire smart park, community, and building.

[0004] This IoT integrated management platform and management method based on a smart brain adopts a data-driven management method and uses data analysis technology to ensure and monitor the normal operation of smart parks, communities, and buildings. Referring to the above method, IoT technology can be used to monitor and repair hospital self-service equipment.

[0005] Therefore, in response to the above problems, there is an urgent need for a smart hospital self-service equipment monitoring and repair method based on the Internet of Things. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a smart hospital self-service equipment monitoring and repair method based on the Internet of Things, which solves the problems of difficulty in sequentially identifying faults of numerous self-service equipment in the hospital, inability to perform priority maintenance, and uneven distribution of maintenance resources.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for monitoring and repairing self-service equipment in a smart hospital based on the Internet of Things, comprising the following steps: based on the real-time monitoring information, basic operating information and extreme weather data analysis of each self-service equipment in the target hospital, obtaining the fault monitoring and early warning value of each self-service equipment in the target hospital; based on the fault monitoring and early warning value of each self-service equipment in the target hospital, prompting the self-service equipment maintenance personnel to perform self-service equipment fault troubleshooting and maintenance, judging whether each self-service equipment in the target hospital has a fault risk, and obtaining the fault troubleshooting and repair order of the self-service equipment with a fault risk in the target hospital; tracking and recording the self-service equipment troubleshooting and maintenance process based on the fault troubleshooting and repair order, and obtaining the maintenance results of each self-service equipment with a fault risk; judging whether the repair report is qualified by analyzing the maintenance results, and performing secondary repair and maintenance for unqualified repair reports.

[0008] Furthermore, the method of judging whether there is a failure risk for each self-service device in the target hospital is as follows: based on the analysis of the historical failure records of the self-service devices in the target hospital, the failure monitoring threshold of the self-service devices in the target hospital is obtained; each self-service device in the target hospital is numbered: a=1, 2, 3, ..., n, a represents the number of each self-service device in the target hospital, and n represents the total number of self-service devices in the target hospital; the failure monitoring and early warning value of each self-service device in the target hospital is compared and analyzed with the failure monitoring threshold of the self-service device in the target hospital; when the failure monitoring and early warning value of the self-service device is greater than the failure monitoring threshold, it is judged that the self-service device has a failure risk and needs further diagnosis and repair, the self-service device that needs diagnosis and repair is marked, and the alarm mechanism is used to prompt the self-service device maintenance personnel to troubleshoot and maintain the self-service device, and the self-service device failure found by the self-service device maintenance personnel is marked; when the failure monitoring and early warning value of the self-service device is less than or equal to the failure monitoring threshold, it is judged that there is no failure in the self-service device and no repair is required, and the real-time monitoring function is used to monitor the self-service device.

[0009] Furthermore, the process of obtaining the fault monitoring and early warning value of each self-service device in the target hospital is specifically as follows: through real-time monitoring record analysis of each self-service device in the target hospital, real-time monitoring information is obtained, and the monitoring characteristic value of each self-service device in the target hospital is obtained by analyzing the real-time monitoring information; through sensor record analysis of each self-service device in the target hospital, basic operation information is obtained, and the operation characteristic value of each self-service device in the target hospital is obtained by analyzing the basic operation information; through environmental information platform record analysis of the area where the target hospital is located, extreme weather data is obtained, and the harsh environment characteristic value of the target hospital is obtained by analyzing the extreme weather data; using the operation characteristic value and the harsh environment characteristic value analysis, the fault prediction index of each self-service device in the target hospital is obtained, and the fault prediction index is used to numerically represent the probability of failure of each self-service device in the target hospital; combining the monitoring characteristic value of each self-service device in the target hospital with the fault prediction index analysis of each self-service device in the target hospital, the fault monitoring and early warning value of each self-service device in the target hospital is obtained.

[0010] Furthermore, the calculation formula for the fault monitoring and early warning value of each self-service device in the target hospital is: Where a represents the number of each self-service device in the target hospital, ζa represents the fault monitoring and early warning value of the a-th self-service device, ηJCa represents the monitoring characteristic value of the a-th self-service device, and ζYCa represents the fault prediction index of the a-th self-service device.

[0011] Furthermore, the real-time monitoring information specifically includes: abnormal power consumption values, communication delay duration, and the specific method for obtaining the monitoring characteristic values ​​of each self-service device in the target hospital is: by analyzing the abnormal power consumption values ​​and the communication delay duration, the monitoring characteristic values ​​of each self-service device in the target hospital are obtained, and the monitoring characteristic values ​​are used to numerically represent the real-time monitoring status of each self-service device in the target hospital; the basic operation information specifically includes: environmental adaptability value, operation fault condition assessment value, and the specific method for obtaining the operation characteristic values ​​of each self-service device in the target hospital is: by analyzing the environmental adaptability value and the operation fault condition assessment value, the operation characteristic values ​​of each self-service device in the target hospital are obtained, and the operation characteristic values ​​are used to numerically represent the basic operation status of each self-service device in the target hospital; the extreme weather data specifically includes: the number of self-service device failures caused by historical extreme weather in the target area, and the specific method for obtaining the harsh environment characteristic values ​​of the target hospital is: by analyzing the number of self-service device failures caused by historical extreme weather in the target area, the harsh environment characteristic values ​​of the target hospital are obtained, and the harsh environment characteristic values ​​are used to numerically represent the predicted situation of severe weather in the area where the target hospital is located.

[0012] Further, the troubleshooting and repair sequence of the self-service equipment at risk of failure in the target hospital is specifically: obtaining a repair deviation value of the self-service equipment at risk of failure in the target hospital, the repair deviation value being used to numerically represent the risk emergency degree of the self-service equipment at risk of failure; sorting the repair deviation values of the self-service equipment at risk of failure in the target hospital according to the size, and sending a priority maintenance prompt information to the self-service equipment maintenance personnel through the Internet of Things for priority troubleshooting and maintenance according to the size of the repair deviation value of the self-service equipment at risk of failure in the target hospital.

[0013] Further, the specific process of obtaining the repair deviation value of the self-service equipment at risk of failure in the target hospital is: obtaining the self-service equipment at risk of failure marked, and numbering the self-service equipment at risk of failure: b = 1, 2, 3,..., m, b represents the number of the self-service equipment at risk of failure, and m represents the total number of the self-service equipment at risk of failure; obtaining the failure monitoring and early warning value of the self-service equipment at risk of failure; obtaining the repair deviation value of the self-service equipment at risk of failure in the target hospital by analyzing the failure monitoring and early warning value and the failure monitoring threshold value of the self-service equipment in the target hospital.

[0014] Further, the specific analysis process of the unqualified repair is: based on the self-service equipment repair data and user data obtained by tracking and recording the self-service equipment failure risk investigation and maintenance process, obtaining the repair effect evaluation value of each self-service equipment at risk of failure in the target hospital; based on the historical self-service equipment failure record of the target hospital and the repair demand analysis of the target hospital, obtaining the repair effect evaluation threshold value of the target hospital; comparing and analyzing the repair effect evaluation value of each self-service equipment at risk of failure in the target hospital with the repair effect evaluation threshold value of the target hospital, when the repair effect evaluation value of the self-service equipment at risk of failure in the target hospital is greater than or equal to the repair effect evaluation threshold value of the target hospital, it is judged that the failure risk investigation and maintenance process and the repair effect of the self-service equipment are qualified, and the repair analysis report of the self-service equipment is automatically generated, the repair analysis report of the self-service equipment including the failure monitoring and early warning value of the self-service equipment and the repair effect evaluation value of the self-service equipment.

[0015] Further, the repair effect evaluation value of each self-service equipment at risk of failure in the target hospital is specifically: the self-service equipment repair data specifically includes: running test stability deviation value, running test vibration abnormal value; by analyzing the running test stability deviation value and the running test vibration abnormal value, the repair effect evaluation value of each self-service equipment at risk of failure in the target hospital is obtained.

[0016] Further, the calculation formula of the repair effect evaluation value of each self-service equipment at risk of failure in the target hospital is: In the formula, ψXGb represents the repair effect evaluation value of the self-service equipment of the target hospital with the bth risk of failure, b represents the number of the self-service equipment with the risk of failure, r 1b represents the running test stable deviation value of the bth self-service equipment with the risk of failure, r 2b represents the running test vibration abnormal value of the bth self-service equipment with the risk of failure, t1 represents the weight factor of the running test stable deviation value, and t2 represents the weight factor of the running test vibration abnormal value.

[0017] The present application has the following beneficial effects:

[0018] (1) The self-service equipment monitoring and repair method based on the Internet of Things can discover potential failure signs of the self-service equipment in advance through real-time monitoring information and extreme weather data analysis, take timely maintenance measures, reduce the impact of sudden failures of the self-service equipment on hospital services, accurately judge the failure conditions of each self-service equipment based on the failure monitoring and early warning values of the self-service equipment, and maintain and repair the self-service equipment in a targeted manner, thereby improving the reliability and stability of the self-service equipment; tracking and recording the maintenance process of the self-service equipment helps to establish a complete self-service equipment maintenance archive, provides a reference for future maintenance, and ensures the standardization and effectiveness of the maintenance process; through analysis of self-service equipment repair data and user feedback, the repair efficiency and service quality of the target hospital can be objectively evaluated, which provides a basis for further optimizing the repair process and improving the service level; through evaluation of the repair efficiency and service quality, the target hospital can discover problems in time and improve the efficiency and accuracy of self-service equipment maintenance, thereby improving the overall service quality and user satisfaction of the hospital.

[0019] (2) The self-service equipment monitoring and repair method based on the Internet of Things uses data processing technology to identify the failure of the self-service equipment of the smart hospital, not only considering the basic operating conditions and real-time monitoring of the self-service equipment, but also considering the impact of adverse weather on the self-service equipment. Comprehensive analysis of the obtained failure monitoring and early warning values can effectively visualize the failure conditions of each self-service equipment, help maintenance personnel quickly and accurately determine whether the self-service equipment needs to be repaired, avoid unnecessary maintenance, improve maintenance efficiency, and save maintenance costs; evaluation of the repair process and hospital maintenance efficiency combines analysis of the running test stable deviation value and the running test vibration abnormal value, not only considering the basic information in the maintenance process, but also combining the subsequent user evaluation, which can help the target hospital realize intelligent management, failure prevention and rapid response of the self-service equipment, improve work efficiency, reduce operating costs, and improve hospital service quality and user experience.

[0020] Of course, implementing any product of the present application does not necessarily require all the advantages described above. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 This is a flow chart of a method for monitoring and repairing self-service equipment in a smart hospital based on the Internet of Things. DETAILED DESCRIPTION

[0022] The embodiment of the present application solves the problems of difficulty in sequentially identifying faults of numerous self-service devices in a hospital, inability to perform priority maintenance, and uneven allocation of maintenance resources through a smart hospital self-service equipment monitoring and repair method based on the Internet of Things.

[0023] The overall approach to the problems in the embodiments of this application is as follows:

[0024] By monitoring the real-time monitoring information and basic operating information of each self-service device in the target hospital, combined with the extreme weather data of the area, the status of the self-service equipment is monitored and analyzed using data analysis technology; based on the monitoring data and analysis results, the fault monitoring and early warning values ​​of each self-service device are obtained, the fault status of the self-service equipment is judged, and the self-service equipment that needs to be repaired is determined, and the self-service equipment that needs maintenance is maintained in sequence to ensure the normal operation of the self-service equipment; in the self-service equipment maintenance process, each step of the operation is tracked and recorded, including maintenance personnel, operation test stability deviation values, maintenance content and other information, to establish a complete maintenance record; based on the tracking records of self-service equipment fault troubleshooting and maintenance process, combined with user repair data and feedback information, data analysis is carried out to evaluate the repair effect of the target hospital, including indicators such as repair response speed, repair accuracy, and user satisfaction; based on the analysis results, the repair efficiency and service quality of the target hospital are evaluated, problems are found and improvement suggestions are put forward, and the repair process and service level are continuously optimized.

[0025] See also Figure 1 , an embodiment of the present invention provides a technical solution: a method for monitoring and repairing self-service equipment in a smart hospital based on the Internet of Things, comprising the following steps: based on analyzing the real-time monitoring information, basic operation information and extreme weather data of each self-service equipment in the target hospital and the area where the target hospital is located, obtaining the fault monitoring and early warning value of each self-service equipment in the target hospital; based on the fault monitoring and early warning value of each self-service equipment in the target hospital, prompting the self-service equipment maintenance personnel to perform self-service equipment fault troubleshooting and maintenance, judging whether each self-service equipment in the target hospital has a fault risk, and obtaining the fault troubleshooting and repair order of the self-service equipment with a fault risk in the target hospital; tracking and recording the self-service equipment troubleshooting and maintenance process based on the fault troubleshooting and repair order, and obtaining the maintenance results of each self-service equipment with a fault risk; judging whether the repair report is qualified by analyzing the maintenance results, and performing a secondary repair and maintenance report for unqualified repair reports.

[0026] Specifically, determine whether there is a failure risk for each self-service device in the target hospital: based on the analysis of the historical self-service device failure records of the target hospital, obtain the failure monitoring threshold of the self-service device in the target hospital; number each self-service device in the target hospital: a=1,2,3,...,n, a represents the number of each self-service device in the target hospital, and n represents the total number of self-service devices in the target hospital; compare and analyze the failure monitoring and early warning value of each self-service device in the target hospital with the failure monitoring threshold of the self-service device in the target hospital; when the failure monitoring and early warning value of the self-service device is greater than the failure monitoring threshold, the self-service device is at a failure risk and requires further diagnosis and repair. The self-service device that requires diagnosis and repair is marked, and an alarm mechanism is used to prompt the self-service device maintenance personnel to troubleshoot and maintain the self-service device, and the self-service device failure found by the self-service device maintenance personnel is marked; when the failure monitoring and early warning value of the self-service device is less than or equal to the failure monitoring threshold, the self-service device does not have a failure condition and does not need to be repaired. The real-time monitoring function is used to monitor the self-service device.

[0027] In this implementation scheme, through the analysis of historical self-service equipment failure records and the setting of self-service equipment failure monitoring thresholds, it is possible to predict possible failures of self-service equipment, so as to take preventive maintenance measures, reduce the occurrence of self-service equipment failures, and improve the reliability and stability of self-service equipment; when the fault monitoring warning value of the self-service equipment is greater than the fault monitoring threshold, the system can automatically mark the self-service equipment that needs to be repaired and promptly notify relevant personnel to carry out repairs, thereby reducing the downtime of the self-service equipment and ensuring the normal operation of the hospital's self-service equipment; the comparative analysis of the self-service equipment fault monitoring warning value and the threshold can help maintenance personnel quickly and accurately determine whether the self-service equipment needs maintenance, avoid unnecessary repairs, improve maintenance efficiency, and save maintenance costs; predictive maintenance and timely repairs can reduce the losses caused by self-service equipment failures, avoid emergency repairs and cost increases caused by self-service equipment failures, and thus reduce the hospital's maintenance costs.

[0028] Specifically, the fault monitoring and early warning values ​​of each self-service device in the target hospital can not only be obtained by further analysis through the machine learning integration model, using integration methods such as K-means clustering model or support vector machine model to combine the diagnosis results of multiple basic models to obtain more accurate fault monitoring and early warning values, but can also be calculated in the following way. The specific analysis process is: through the real-time monitoring record analysis of each self-service device in the target hospital, the real-time monitoring information is obtained, and the real-time monitoring information is used for analysis to obtain the monitoring characteristic value of each self-service device in the target hospital; through the sensor record analysis of each self-service device in the target hospital, the basic operation information is obtained, and the basic operation information is used for analysis to obtain the operation characteristic value of each self-service device in the target hospital. The operation characteristic value is obtained by analyzing the records of the environmental information platform in the area where the target hospital is located, and the extreme weather data is obtained by analyzing the extreme weather data. The characteristic value of the harsh environment of the target hospital is obtained; the operation characteristic value and the characteristic value of the harsh environment are analyzed to obtain the failure prediction index of each self-service device in the target hospital. The failure prediction index is used to numerically represent the probability of failure of each self-service device in the target hospital. The failure prediction index can not only be obtained by further analysis of the machine learning integration model, but also by combining the prediction results of multiple basic models using integration methods such as K-means clustering model or support vector machine model to obtain a more accurate failure prediction index. It can also be calculated in the following way. The specific calculation formula is:

[0029] In the formula represents the failure prediction index of the a-th self-service device. The failure prediction index of each self-service device in the target hospital is used to numerically represent the probability of failure of each self-service device in the target hospital. ηYXa represents the operating characteristic value of the a-th self-service device, ηHJ represents the harsh environment characteristic value of the target hospital, s1 represents the weight factor of the operating characteristic value, and s2 represents the weight factor of the harsh environment characteristic value. Combined with the monitoring characteristic value of each self-service device in the target hospital and the failure prediction index analysis of each self-service device in the target hospital, the fault monitoring and early warning value of each self-service device in the target hospital is obtained.

[0030] In this implementation plan, through comprehensive analysis of real-time monitoring information, operating characteristic values ​​and environmental characteristic values, early warning of possible failures of self-service equipment can be achieved, which helps to take timely maintenance measures and avoid the impact of self-service equipment failures on the normal operation of the hospital; combined with the analysis results of monitoring characteristic values, operating characteristic values ​​and environmental characteristic values, more accurate self-service equipment maintenance strategies can be formulated, including regular maintenance, preventive maintenance and emergency maintenance, thereby improving maintenance efficiency and reducing; through timely analysis of the fault monitoring and early warning values ​​of self-service equipment, problems with self-service equipment can be discovered and repaired in a timely manner, thereby improving the reliability and stability of self-service equipment and ensuring the normal operation of the hospital's self-service equipment; early warning faults and optimized maintenance strategies can reduce the downtime of self-service equipment, reduce production losses caused by self-service equipment failures, and ensure the normal operation of the hospital; through analysis of environmental characteristic values, the impact of harsh environmental factors such as extreme weather on the operation of self-service equipment can be discovered in a timely manner, the safety management of self-service equipment can be strengthened, and accidents can be prevented.

[0031] Specifically, the calculation formula for the fault monitoring warning value of each self-service device in the target hospital is:

[0032] Where a represents the number of each self-service device in the target hospital, ζa represents the fault monitoring and warning value of the a-th self-service device, represents the monitoring characteristic value of the a-th self-service device, represents the failure prediction index of the a-th self-service device.

[0033] In this implementation plan, the fault monitoring and early warning values ​​can help hospitals predict possible faults in self-service equipment, allowing maintenance personnel to take measures in advance to reduce the downtime of self-service equipment and improve the availability and efficiency of self-service equipment; by analyzing the fault monitoring and early warning values, more accurate self-service equipment maintenance strategies can be formulated, including regular maintenance, preventive maintenance and emergency maintenance, thereby improving maintenance efficiency and reducing; timely detection of possible faults in self-service equipment helps to reduce the impact of self-service equipment failures on the normal operation of the hospital and improve the reliability and stability of self-service equipment; the setting of weight factors for monitoring characteristic values, operating characteristic values ​​and harsh environment characteristic values ​​depends on the evaluation of monitoring characteristic values, operating characteristic values ​​and harsh environment characteristic values ​​by experts in related fields, and setting them according to their importance and impact, or using historical data analysis methods, through statistical analysis and machine learning technology, to mine the impact of each characteristic value on the self-service equipment failure from the data, so as to determine the weight.

[0034] Specifically, real-time monitoring information includes: abnormal power consumption values, communication delay duration, and monitoring characteristic values ​​of each self-service device in the target hospital. In addition to being obtained through analysis on the smart hospital self-service device monitoring information analysis platform, it can also be obtained through a more accurate calculation method. The specific calculation method is as follows: By analyzing abnormal power consumption values ​​and communication delay duration, the monitoring characteristic values ​​of each self-service device in the target hospital are obtained. The calculation formula is:

[0035] Where ηJCa represents the monitoring characteristic value of the ath self-service device, which is used to numerically represent the real-time monitoring status of each self-service device in the target hospital. q represents the sampling period number of the monitoring characteristic value, q = 1, 2, 3, ..., h, h represents the total number of sampling periods of the monitoring characteristic value, a represents the number of each self-service device in the target hospital, and ε 1qa represents the abnormal power consumption value of the a-th self-service device in the q-th monitoring characteristic value sampling period, ε 2qa represents the communication delay of the ath self-service device in the qth monitoring characteristic value sampling period, ε 1o represents the average power consumption, ε 2o represents the average value of communication delay, g1 represents the weighting factor of abnormal power consumption, and g2 represents the weighting factor of communication delay duration.

[0036] The basic operation information specifically includes: environmental adaptability value, operation fault condition assessment value. In addition to being obtained through the smart hospital self-service equipment monitoring information analysis platform, the operation characteristic value of each self-service device in the target hospital can also be obtained through a more accurate calculation method. The specific calculation method is as follows: By analyzing the environmental adaptability value and the operation fault condition assessment value, the operation characteristic value of each self-service device in the target hospital is obtained. The calculation formula is:

[0037] In the formula It represents the operating characteristic value of the a-th self-service device, which is used to numerically represent the basic operating status of each self-service device in the target hospital. a represents the number of each self-service device in the target hospital. represents the environmental adaptability value of the a-th self-service device, represents the operational fault status evaluation value of the a-th self-service device, τ1 represents the weight factor of the environmental adaptability value, and τ2 represents the weight factor of the operational fault status evaluation value.

[0038] The extreme weather data specifically includes: the number of self-service equipment failures caused by historical extreme weather in the target area, and the harsh environment characteristic value of the target hospital can be obtained through more accurate calculation methods in addition to being obtained through the intelligent hospital self-service equipment monitoring information analysis platform. The specific calculation method is as follows: the number of self-service equipment failures caused by historical extreme weather in the target area is analyzed to obtain the harsh environment characteristic value of the target hospital, and the calculation formula is:

[0039] In the formula, ηHJ represents the harsh environment characteristic value of the target hospital, which is used to numerically represent the prediction condition of the occurrence of adverse weather in the area where the target hospital is located, y represents the sampling period number of the harsh environment characteristic value, y = 1, 2, 3, …, y', y' represents the total number of harsh environment characteristic value sampling periods, and βy represents the number of self-service equipment failures caused by historical extreme weather in the yth harsh environment characteristic value sampling period.

[0040] In this embodiment, the power consumption outlier represents the case that the power consumption of the Internet of Things self-service device deviates from the normal level during the working process. Detecting the power consumption outlier can help to find the failure or abnormal situation of the self-service device. The real-time power consumption data of the self-service device is recorded by the power sensor or the electric meter connected to the self-service device, and an algorithm or rule is used to detect whether there is an outlier for acquisition. The communication delay duration represents the delay time of the communication between the Internet of Things self-service device and the server or other self-service devices. The increase of the communication delay duration will affect the real-time and accuracy of the self-service device data, and even cause communication failure or data loss. The time stamp of the communication between the self-service device and the server or other self-service devices is recorded, and the communication delay time is calculated to obtain the communication delay duration for acquisition. The power consumption average value refers to the average level of the power consumption of the self-service device in a certain time range, which is used to evaluate the energy utilization efficiency, monitor the energy consumption trend, and develop energy-saving strategies. The power consumption is monitored in real time by installing power metering self-service devices, using energy monitoring systems or smart meters, etc., and the average value is obtained by aggregating and calculating the data. The communication response average duration represents the average time length of the system or self-service device response when communicating, which is used to evaluate the communication performance and efficiency of the system. The time stamps of the communication request and response are recorded, and the time difference between them is calculated, and then the average of these time differences is calculated for acquisition. The environmental adaptability value represents the adaptability value of the self-service device to the environment. The running state of the self-service device in response to various extreme environments is analyzed, and a comprehensive score is given by relevant field professionals. The running failure condition evaluation value represents the evaluation value of the running failure of the self-service device. The frequency of failure is analyzed by recording the failure events of the self-service device, and the running failure condition evaluation value is set by relevant field professionals in combination with the self-service device maintenance record. If the self-service device does not have a maintenance record, the running failure condition evaluation value is 0. The number of self-service device failures caused by extreme weather in the target area represents the number of self-service device failures caused by extreme weather events such as heavy rain, storm, high temperature, etc. in the target area in the past period of time, which can help to evaluate the stability and reliability of the self-service device under extreme weather conditions. The number of self-service device failures caused by extreme weather in the target area is obtained by analyzing the historical data, and the number of self-service device failures during or after the extreme weather event is counted to obtain the number of self-service device failures caused by extreme weather in the target area for acquisition. The setting of the weight factor depends on the cognition of the relevant field professionals based on the identification of self-service device failures.

[0041] Specifically, the order of troubleshooting and repairing the self-service equipment with failure risks in the target hospital is as follows: obtaining the repair deviation value of the self-service equipment with failure risks in the target hospital, which is used to numerically represent the risk urgency of the self-service equipment with failure risks; sorting the repair deviation values ​​of the self-service equipment with failure risks in the target hospital, and sending priority maintenance reminder information to the self-service equipment maintenance personnel through the Internet of Things according to the size of the repair deviation values ​​of the self-service equipment with failure risks in the target hospital to perform priority troubleshooting and maintenance.

[0042] In this implementation plan, by quantifying the repair deviation values ​​of self-service equipment and sorting them according to their size, it can help hospitals allocate maintenance resources more effectively. High-priority self-service equipment will be repaired first, thereby minimizing the impact of self-service equipment failures on the normal operation of the hospital; priority maintenance reminder information is sent to self-service equipment maintenance personnel through the Internet of Things, which can achieve real-time maintenance reminders and notifications, helping to improve the maintenance response speed. Maintenance personnel can immediately know the self-service equipment that needs priority repair, so as to take quick action and reduce the downtime of self-service equipment; by giving priority to the maintenance of high-priority self-service equipment, the losses caused to the hospital by self-service equipment failure can be minimized, including production losses and repair costs. Timely repairs can prevent the fault from further deteriorating and reduce the complexity and cost of repairs.

[0043] Specifically, in addition to being obtained through analysis on the smart hospital self-service equipment monitoring information analysis platform, the repair deviation value of the target hospital's self-service equipment with failure risks can also be obtained through a more accurate calculation method. The specific calculation method is as follows: obtain the marked self-service equipment with failure risks and number the self-service equipment with failure risks: b = 1, 2, 3, ..., m, where b represents the number of the self-service equipment with failure risks and m represents the total number of self-service equipment with failure risks; obtain the fault monitoring and early warning value of the self-service equipment with failure risks; and obtain the repair deviation value of the target hospital's self-service equipment with failure risks by analyzing the fault monitoring and early warning value with the fault monitoring threshold of the target hospital's self-service equipment. The calculation formula is: Where χ b represents the repair deviation value of the bth self-service device with failure risk, ζ b represents the fault monitoring warning value of the bth self-service device with failure risk, and ζ' represents the fault monitoring threshold.

[0044] In this implementation scheme, by marking and numbering self-service equipment that is at risk of failure, the self-service equipment that needs maintenance can be quickly identified, and clear guidance can be provided to maintenance personnel, thereby speeding up the speed and efficiency of maintenance. Obtaining the fault monitoring and early warning values ​​of self-service equipment that is at risk of failure can help hospitals to promptly detect failures or abnormal conditions of self-service equipment. By monitoring and analyzing these values, early warning and preventive maintenance can be performed before the self-service equipment fails, thereby reducing the impact of self-service equipment failures on hospital operations; by comparing and analyzing the fault monitoring and early warning values ​​of self-service equipment that is at risk of failure with the fault monitoring thresholds of the target hospital's self-service equipment, the repair deviation value of the self-service equipment can be obtained. Such quantitative assessment can help hospitals determine the maintenance priority of self-service equipment, give priority to those self-service equipment with higher repair deviation values, thereby minimizing the impact of self-service equipment failures on the normal operation of the hospital, and improving the overall operating efficiency and service level of the hospital.

[0045] Specifically, the specific analysis process for secondary repair maintenance for unqualified repair reports is as follows: based on the self-service equipment repair data and user data obtained by tracking and recording the self-service equipment fault risk investigation and maintenance process, the repair effect evaluation value of each self-service equipment with fault risk in the target hospital is obtained; based on the analysis of the historical self-service equipment fault records and the repair demand of the target hospital, the repair effect evaluation threshold of the target hospital is obtained; the repair effect evaluation value of each self-service equipment with fault risk in the target hospital is compared and analyzed with the repair effect evaluation threshold of the target hospital. When the repair effect evaluation value of the self-service equipment with fault risk in the target hospital is greater than or equal to the repair effect evaluation threshold of the target hospital, it is judged that the fault risk investigation and maintenance process and the repair effect of the self-service equipment are qualified, and a repair analysis report for the self-service equipment is automatically generated. The repair analysis report of the self-service equipment includes the fault monitoring and early warning value of the self-service equipment and the repair effect evaluation value of the self-service equipment.

[0046] In this implementation plan, by analyzing historical self-service equipment failure records and repair needs and setting a repair effect assessment threshold, the target hospital's self-service equipment maintenance process can be optimized. When the repair effect assessment value reaches or exceeds the threshold, it indicates that the self-service equipment maintenance performance is good and meets the repair needs, and no additional intervention is required. This helps ensure the efficiency and accuracy of the self-service equipment maintenance process; when the target hospital's repair effect meets the requirements, the system will automatically generate a repair analysis report, which can provide hospital managers with real-time self-service equipment maintenance status and performance indicators to help them make more informed decisions; when the repair effect assessment value is lower than the set threshold, the system triggers an alarm mechanism and sends a text message reminder to the self-service equipment maintenance personnel, which helps to promptly identify problems in the self-service equipment maintenance process and encourages self-service equipment maintenance personnel to optimize the self-service equipment configuration and maintenance plan to improve the efficiency and quality of self-service equipment maintenance. Promptly identifying problems in the self-service equipment maintenance process and optimizing them can reduce self-service equipment.

[0047] Specifically, the repair effect evaluation value of each self-service equipment with failure risk in the target hospital can not only be obtained by further analysis through the machine learning integration model, using integration methods such as K-means clustering model or support vector machine model to combine the evaluation results of multiple basic models to obtain a more accurate repair effect evaluation value, but can also be calculated in the following way. The specific analysis process is: the self-service equipment repair data includes: operation test stability deviation value, operation test vibration abnormality value; by analyzing the operation test stability deviation value and the operation test vibration abnormality value, the repair effect evaluation value of each self-service equipment with failure risk in the target hospital is obtained.

[0048] In this implementation plan, by analyzing the operation test stability deviation value and the operation test vibration abnormality value, the target hospital's repair effect can be comprehensively evaluated. These indicators cover all aspects of the repair process, from repair speed to repair quality to user satisfaction, providing the hospital with comprehensive evaluation indicators; by comparing and analyzing different indicators, the advantages and disadvantages of the target hospital's repair process can be discovered. By analyzing the operation test vibration abnormality value, the user's satisfaction with the self-service equipment repair process can be understood. Based on user feedback, the hospital can improve the maintenance process and improve service quality in a targeted manner, thereby improving user satisfaction and trust. The analysis of the operation test stability deviation value and can help the hospital optimize resource allocation. If it is found that the operation test stability deviation value of some self-service equipment is too long or too high, you can consider adjusting the configuration of the maintenance team or improving the maintenance process to improve resource utilization efficiency.

[0049] Specifically, the calculation formula for the repair effect evaluation value of each self-service device with failure risk in the target hospital is: Where ψ XGbrepresents the repair effect evaluation value of the bth self-service device with failure risk in the target hospital, b represents the number of the self-service device with failure risk, r 1b represents the stable deviation value of the running test of the bth self-service device with failure risk, r 2b represents the abnormal vibration value of the operation test of the bth self-service device with failure risk, t1 represents the weight factor of the operation test stability deviation value, and t2 represents the weight factor of the abnormal vibration value of the operation test.

[0050] In this implementation scheme, the operation test stability deviation value is usually used to describe the degree of deviation of the equipment from the average during long-term operation. If the output value of the equipment fluctuates less within a period of time, the stability deviation value will be relatively small, indicating that the output of the equipment is relatively stable. The smaller the stability deviation value, the more stable the performance of the equipment; the operation test vibration abnormality value is used to describe the vibration of the equipment during operation. It is normal for the equipment to have a certain degree of vibration when it is operating normally, but if the vibration abnormality value exceeds the set threshold or normal range, it means that the equipment has vibration abnormality; the operation test stability deviation value and the operation test vibration abnormality value are calculated by installing sensors on the equipment, monitoring the data output by the equipment, and calculating the stability deviation value and vibration abnormality value through the data collected by the sensors, or using the equipment monitoring system to monitor and record the equipment operation status in real time. The system can automatically calculate and alarm whether the stability deviation value and vibration abnormality value exceed the set threshold; the setting of the weight factor is determined by the hospital management or relevant professionals based on actual conditions and needs, and the weight can be set according to the importance and impact of each indicator on the hospital operation.

[0051] In summary, this application has at least the following effects:

[0052] By analyzing the real-time monitoring information and extreme weather data of each self-service device in the target hospital, possible failures of the self-service equipment can be discovered in advance and predicted, which helps to take timely measures to avoid unnecessary losses and impacts caused by failures of self-service equipment; by troubleshooting and maintaining self-service equipment based on fault monitoring and early warning values, targeted maintenance can be carried out according to the actual situation and needs of the self-service equipment, improving the accuracy and efficiency of maintenance and extending the service life of the self-service equipment; by tracking and recording the maintenance process of self-service equipment and combining user repair data and user feedback information, the repair effect of self-service equipment at risk in the target hospital can be evaluated, which helps to discover problems in the maintenance process and timely improve and optimize service processes, thereby improving service quality and user satisfaction; through real-time monitoring, fault prediction, intelligent maintenance and repair effect evaluation, the maintenance efficiency and service quality of the target hospital's self-service equipment can be effectively improved, making the operation of the hospital's self-service equipment more stable and reliable, reducing failure downtime, and improving the hospital's overall operating efficiency and service level.

[0053] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0054] The present invention is described with reference to flowcharts of methods according to embodiments of the present invention. It should be understood that each combination of processes in the flowcharts can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts. Figure 1 A device that specifies functions in a process or multiple processes.

[0055] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A function specified in a process or multiple processes.

[0056] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 The steps of a specified function in a process or multiple processes.

[0057] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0058] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for monitoring and repairing self-service equipment in a smart hospital based on the Internet of Things, characterized in that: The following steps are involved: Based on the real-time monitoring information, basic operating information and extreme weather data analysis of each self-service device in the target hospital, the fault monitoring and early warning values ​​of each self-service device in the target hospital are obtained; The process of obtaining the fault monitoring and warning values ​​of each self-service device in the target hospital is specifically as follows: By analyzing the real-time monitoring records of each self-service device in the target hospital, real-time monitoring information is obtained, and the monitoring characteristic values ​​of each self-service device in the target hospital are obtained by analyzing the real-time monitoring information; The real-time monitoring information specifically includes: abnormal power consumption values, communication delay duration, and the monitoring characteristic values ​​of each self-service device in the target hospital are specifically obtained in the following manner: By analyzing abnormal power consumption values ​​and communication delay duration, the monitoring characteristic values ​​of each self-service device in the target hospital are obtained. The monitoring characteristic values ​​are used to numerically represent the real-time monitoring status of each self-service device in the target hospital. The calculation formula is: ； In the formula Indicates the The monitoring characteristic values ​​of each self-service device are used to numerically represent the real-time monitoring status of each self-service device in the target hospital. Indicates the sampling period number of the monitoring characteristic value, , Indicates the total number of monitoring characteristic value sampling cycles, Indicates the number of each self-service device in the target hospital. Indicates the The first monitoring characteristic value sampling period Abnormal power consumption of self-service equipment, Indicates the The first monitoring characteristic value sampling period The communication delay of each self-service device, Indicates the average power consumption, Indicates the average communication delay time, represents the weight factor of abnormal values ​​of power consumption, A weight factor representing the length of communication delay; By analyzing the sensor records of each self-service device in the target hospital, basic operation information is obtained, and the operation characteristic values ​​of each self-service device in the target hospital are obtained by analyzing the basic operation information; The basic operation information specifically includes: environmental adaptability value, operation fault condition assessment value, and the operation characteristic value of each self-service device in the target hospital is specifically obtained in the following manner: By analyzing the environmental adaptability value and the operational fault status evaluation value, the operational characteristic value of each self-service device in the target hospital is obtained. The operational characteristic value is used to numerically represent the basic operational status of each self-service device in the target hospital; Obtain extreme weather data through analysis of records on the environmental information platform of the target hospital's area, and use this data to analyze the harsh environmental characteristics of the target hospital; The extreme weather data specifically includes: the number of self-service equipment failures caused by historical extreme weather in the target area. The specific method for obtaining the harsh environment characteristic value of the target hospital is: By analyzing the number of self-service equipment failures caused by historical extreme weather in the target area, the target hospital's adverse environment characteristic value is obtained. The adverse environment characteristic value is used to numerically represent the predicted situation of adverse weather in the target hospital's area; By analyzing the operating characteristic values ​​and the adverse environment characteristic values, we can obtain the failure prediction index of each self-service device in the target hospital. The failure prediction index is used to numerically represent the probability of failure of each self-service device in the target hospital. Combine the monitoring characteristic values ​​of each self-service device in the target hospital with the fault prediction index analysis of each self-service device in the target hospital to obtain the fault monitoring and early warning values ​​of each self-service device in the target hospital; Based on the fault monitoring and early warning values ​​of each self-service device in the target hospital, the self-service device maintenance personnel are prompted to perform self-service device fault troubleshooting and maintenance, determine whether each self-service device in the target hospital has a fault risk, and obtain the fault troubleshooting and repair sequence of self-service devices with a fault risk in the target hospital; Track and record the troubleshooting and maintenance process of self-service equipment based on the troubleshooting and repair order, and obtain maintenance results for each self-service equipment with failure risks; By analyzing the maintenance results, we can determine whether the repair is qualified, and conduct secondary repair and maintenance if the repair is unqualified.

2. The method for monitoring and repairing self-service equipment in a smart hospital based on the Internet of Things according to claim 1, characterized in that: The specific steps for determining whether there is a failure risk for each self-service device in the target hospital are: Based on the analysis of the historical self-service equipment failure records of the target hospital, the failure monitoring threshold of the target hospital's self-service equipment is obtained; Number each self-service device in the target hospital: , Indicates the number of each self-service device in the target hospital. It is expressed as the total number of self-service devices in the target hospital; Compare and analyze the fault monitoring and warning values ​​of each self-service device in the target hospital with the fault monitoring threshold of the target hospital's self-service devices. When the fault monitoring and warning value of a self-service device is greater than the fault monitoring threshold, it is determined that the self-service device is at risk of failure and requires further diagnosis and repair. The self-service device that requires diagnosis and repair is marked, and an alarm mechanism is used to prompt the self-service device maintenance personnel to troubleshoot and maintain the self-service device. The self-service device faults found by the self-service device maintenance personnel are marked. When the fault monitoring warning value of the self-service device is less than or equal to the fault monitoring threshold, it is determined that there is no fault condition in the self-service device and no repair is required. The self-service device is monitored using the real-time monitoring function.

3. The method for monitoring and repairing self-service equipment in a smart hospital based on the Internet of Things according to claim 2 is characterized in that: The calculation formula for the fault monitoring and early warning value of each self-service device in the target hospital is: ； In the formula Indicates the number of each self-service device in the target hospital. Indicates the Fault monitoring and early warning value of each self-service device, Indicates the Monitoring characteristic values ​​of self-service devices, Indicates the The failure prediction index of self-service equipment.

4. The method for monitoring and repairing self-service equipment in a smart hospital based on the Internet of Things according to claim 1 is characterized in that: The specific order of troubleshooting and repairing the self-service equipment with failure risk in the target hospital is as follows: Obtaining a repair deviation value for self-service equipment at a target hospital that is at risk of failure, wherein the repair deviation value is used to numerically represent the risk urgency of the self-service equipment at risk of failure; The repair deviation values ​​of the self-service equipment with failure risks in the target hospital are sorted by size, and priority maintenance prompt information is sent to the self-service equipment maintenance personnel through the Internet of Things according to the size of the repair deviation values ​​of the self-service equipment with failure risks in the target hospital for priority fault troubleshooting and maintenance.

5. The method for monitoring and repairing self-service equipment in a smart hospital based on the Internet of Things according to claim 4 is characterized in that: The specific process of obtaining the repair deviation value of the self-service equipment with failure risk in the target hospital is as follows: Obtain the marked self-service devices that are at risk of failure and number them: , Indicates the number of the self-service device that is at risk of failure. The total number of self-service devices that are at risk of failure; Obtain fault monitoring and early warning values ​​for self-service devices with failure risks; By analyzing the fault monitoring warning value and the fault monitoring threshold of the target hospital's self-service equipment, the repair deviation value of the target hospital's self-service equipment with fault risks is obtained.

6. The method for monitoring and repairing self-service equipment in a smart hospital based on the Internet of Things according to claim 1 is characterized in that: The specific analysis process of performing secondary repair and maintenance on unqualified repair reports is as follows: Based on the analysis of self-service equipment repair data obtained by tracking and recording the self-service equipment failure risk investigation and maintenance process, the repair effect evaluation value of each self-service equipment with failure risk in the target hospital is obtained; Based on the analysis of the target hospital's historical self-service equipment failure records and the target hospital's repair demand, the target hospital's repair effect assessment threshold is obtained; Comparing and analyzing the repair effect evaluation value of each self-service device with a fault risk in the target hospital with the repair effect evaluation threshold of the target hospital, when the repair effect evaluation value of the self-service device with a fault risk in the target hospital is greater than or equal to the repair effect evaluation threshold of the target hospital, it is determined that the fault risk investigation and maintenance process and the repair effect of the self-service device are qualified, and a repair analysis report for the self-service device is automatically generated, wherein the repair analysis report for the self-service device includes the fault monitoring and early warning value of the self-service device and the repair effect evaluation value of the self-service device; When the repair effect evaluation value of the self-service equipment with failure risk in the target hospital is less than the repair effect evaluation threshold of the target hospital, the repair is judged to be unqualified, and an alarm mechanism is used to send a text message to prompt the self-service equipment maintenance personnel to optimize the configuration of the self-service equipment and perform secondary repair maintenance.

7. The method for monitoring and repairing self-service equipment in a smart hospital based on the Internet of Things according to claim 1 is characterized in that: The repair effect evaluation values ​​of the self-service equipment with failure risks in the target hospital are as follows: The self-service equipment repair data specifically includes: operation test stability deviation value, operation test vibration abnormality value; By analyzing the operation test stability deviation value and the operation test vibration abnormality value, the repair effect evaluation value of each self-service equipment with failure risk in the target hospital is obtained.

8. The method for monitoring and repairing self-service equipment in a smart hospital based on the Internet of Things according to claim 1 is characterized in that: The calculation formula for the repair effect evaluation value of each self-service device with failure risk in the target hospital is: ； In the formula Indicates the target hospital The repair effect evaluation value of a self-service device with a risk of failure, Indicates the number of the self-service device that is at risk of failure. Indicates the The stable deviation value of the operation test of the self-service equipment with failure risk, Indicates the Abnormal vibration values ​​during operation tests of self-service equipment with a risk of failure, The weight factor representing the stable deviation value of the running test, Indicates the weighting factor for vibration outliers in a test run.

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