CN116962471A - Medical equipment management system based on Internet of things - Google Patents

Abstract

The invention belongs to the technical field of medical equipment management, in particular to a medical equipment management system based on the Internet of things, which comprises an intelligent hardware module, wherein the intelligent hardware module is used for monitoring the operation of medical equipment and comprises a processor, a data communication test module, a multi-component joint monitoring module, a time-of-operation detection judgment module and an early warning management module; according to the invention, the intelligent hardware module is used for monitoring the operation of the medical equipment, carrying out data communication test before the operation, ensuring the communication stability to ensure the normal data transmission, and synchronously monitoring and accurately analyzing a plurality of equipment parts of the medical equipment, so that a manager can master the operation safety condition of the equipment parts, the operation risk of the medical equipment is reduced, the service life of the medical equipment is prolonged, and the medical equipment is detected and analyzed during the operation of all the equipment parts to predict the continuous operation risk degree of the medical equipment when all the equipment parts are normal, thereby being beneficial to further reducing the operation risk of the medical equipment.

Description

A medical equipment management system based on the Internet of Things

Technical field

The present invention relates to the technical field of medical equipment management, specifically a medical equipment management system based on the Internet of Things.

Background technique

Medical equipment refers to instruments, equipment, appliances, in vitro diagnostic reagents and calibrators, materials and other similar or related items that are used directly or indirectly on the human body. Medical equipment is very wide in terms of type and function. With the With the continuous development of modern medicine and medical technology, medical equipment is also constantly updated and improved. From the initial simple instruments to modern high-tech medical equipment, the advancement of medical equipment provides more accurate and efficient methods for medical diagnosis and treatment, providing Provide better protection for human health and disease treatment;

At present, when large-scale medical equipment is put into use, it is difficult to grasp its data communication performance in detail before operation and perform communication optimization in a timely manner. This is not conducive to ensuring the stability and efficiency of data communication during operation, and it is impossible to monitor large-scale medical equipment during operation. The operating status of the equipment is comprehensively and effectively monitored, and it is difficult to predict the risk of continued operation when each equipment component is operating normally. Managers are unable to perform diagnostic inspections of corresponding equipment components and stop medical equipment in a timely manner, which is not conducive to ensuring large-scale medical care. Stable and safe operation of equipment makes it difficult for managers to effectively manage the operation and management of medical equipment;

In view of the above technical defects, a solution is proposed.

Contents of the invention

The purpose of the present invention is to provide a medical equipment management system based on the Internet of Things, which solves the problem in the existing technology that it is difficult to grasp its data communication performance in detail before operation and to perform communication optimization in a timely manner, and it is unable to perform large-scale medical equipment management during operation. Comprehensive and effective monitoring of the operating status of the equipment, and the difficulty in predicting the degree of risk of continued operation when each equipment component is operating normally, are not conducive to ensuring the stable and safe operation of large medical equipment.

In order to achieve the above objects, the present invention provides the following technical solutions:

A medical equipment management system based on the Internet of Things, including intelligent hardware modules. The intelligent hardware modules monitor the operation of medical equipment and perform two-way data transmission through the Internet of Things. Among them, the intelligent hardware modules include processors, data communication tests module, multi-component joint monitoring module, operation time detection and judgment module, and early warning management module; before the corresponding medical equipment starts running, the data communication test module performs a response test on the data communication between the intelligent hardware module and the corresponding medical equipment, and performs a response test on When qualified, a communication stress test is performed to evaluate the stability and communication performance of data communication, and when a response test failure signal or stress test failure signal is generated, it is sent to the early warning management module through the processor, and the early warning management module receives A corresponding early warning is issued when responding to a test failure signal or a stress test failure signal; after the communication test is completed and it is judged that the response test is qualified and the communication stress test is qualified, the corresponding medical equipment starts running;

During the operation of medical equipment, the multi-component joint monitoring module collects the equipment components that need to be monitored in the corresponding medical equipment, and marks the corresponding equipment components as monitoring objects i, i={1,2,...,n}, n represents The number of equipment components to be monitored and n is a positive integer greater than 1; and the monitoring object i is subjected to operation detection and analysis, based on which it is judged whether the monitoring object i is running abnormally, and a monitoring early warning signal is generated when the monitoring object i is judged to be running abnormally, And the monitoring early warning signal is sent to the early warning management module through the processor, and the early warning management module issues a corresponding early warning after receiving the monitoring early warning signal; if all equipment components of the corresponding medical equipment are operating normally, the operation time detection and judgment module will operate the corresponding medical equipment. Real-time detection and analysis, based on which a qualified or unqualified signal for the corresponding medical equipment is generated. The unqualified signal for the corresponding medical equipment is sent to the early warning management module through the processor. The early warning management module receives the unqualified signal for the unqualified operation. A corresponding early warning is issued when a qualified signal is received.

Further, the specific test process of the data communication test module includes:

Send a data request to the corresponding medical device through the data sender. After sending the data request, wait for the corresponding medical device to respond, receive the response data of the corresponding medical device through the receiver, and judge the corresponding medical device based on the expected response mode and response time. Whether the response is normal. If the corresponding medical device can return response data within the expected response time, and the data format and content of the response data are correct, then the data transmission is judged to be normal and a response test pass signal is generated. Otherwise, the data transmission is judged to be abnormal and a response test is generated. Failure signal;

When judging that the data transmission is normal, use the data generator to generate a large amount of test data, simulate different data transmission load conditions through the simulator, and adjust and optimize according to the expected load conditions to simulate the data transmission load in actual usage scenarios; after generating Conduct a communication stress test under the test data and imposed load conditions, record the time, rate and stability indicators of data transmission, and compare the recorded test results with the corresponding expected results to judge the communication performance and stability performance. When the communication performance and stability are normal, a stress test passing signal is generated, otherwise a stress test failing signal is generated.

Further, the specific analysis process of running detection analysis is as follows:

The processor retrieves the preset monitoring parameters of monitoring object i and the parameter data requirements of the corresponding monitoring parameters, obtains the real-time parameter data of each monitoring parameter corresponding to the monitoring object i at the detection time point, and compares the real-time parameter data with the corresponding Parameter data requirements are compared. If all real-time parameter data meet the parameter data requirements, it is judged that the monitoring object i at the detection time point is running qualified; otherwise, the corresponding parameter data that does not meet the parameter data requirements will be marked as unqualified parameter data and will be unqualified. The deviation value of parameter data compared with the corresponding parameter data requirements is marked as an unqualified difference value;

The preset deviation risk value of the corresponding monitoring parameter that is set in advance is retrieved from the processor, the unqualified difference value is multiplied by the corresponding preset deviation risk value to calculate the risk value, and all parameters of the monitoring object i at the detection time point are calculated. The total insurance value is calculated by summing the risk values; the total insurance value is numerically compared with the preset total insurance value threshold of the monitoring object i. If the total insurance value exceeds the preset total insurance value threshold, the detection is judged The operation of the monitoring object i at the time point is unqualified. If the total insurance value does not exceed the preset total insurance value threshold, the monitoring object i at the time of detection is judged to be running qualified.

Further, after judging whether the monitoring object i is running qualified or unqualified at the detection time point, the detection time point corresponding to the unqualified operation of the monitoring object i within the unit time is marked as the analysis time point; the adjacent two sets of analysis time points are marked The time interval is marked as the analysis interval length. The total insurance value at the analysis time point is subtracted from the preset total insurance value threshold to obtain the insurance excess value. All insurance excess values are summed and averaged to obtain the parameter value. To calculate the average insurance excess, sum up all analysis intervals and take the average to obtain the time-sharing average; numerically calculate the insurance excess average and the time-sharing average to obtain the operation inspection value of the monitored object i; combine the operation inspection value with the time-sharing average The preset operation inspection threshold of the monitoring object i is compared numerically. If the operation inspection value exceeds the preset operation inspection threshold, it is judged that the monitoring object i is operating abnormally and a monitoring early warning signal is generated.

Further, the specific analysis process of timing detection and analysis includes:

Obtain the current running start time of the corresponding medical equipment, calculate the time difference between the current time and the current running start time to obtain the current running time, and compare the current running time with the preset single running time threshold. If this time If the running time exceeds the preset single running time threshold, an abnormal operation signal will be generated; if the running time does not exceed the preset single running time threshold, the power curve of the current running of the corresponding medical equipment will be collected, with time as Axis and operating power establish a power rectangular coordinate system for the Y-axis, and place the power curve into the power rectangular coordinate system;

In the power rectangular coordinate system, draw the power determination ray parallel to the The risk duration and the current running duration are numerically calculated to obtain the duration performance value, and the duration performance value is numerically compared with the preset duration performance threshold. If the duration performance value exceeds the preset duration performance threshold, an operation time abnormal signal is generated.

Further, if the duration performance value does not exceed the preset duration performance threshold, the time difference between the end time of the last run of the corresponding medical equipment and the start time of this run is calculated to obtain the run interval length, and the time difference between the adjacent runs of the corresponding medical equipment is collected. For the duration performance value of a run, calculate the ratio of the duration performance value to the running interval length to obtain the operation shutdown abnormal value. Compare the operation shutdown abnormal value with the preset operation shutdown exception threshold. If the operation shutdown abnormal value does not exceed the preset operation shutdown abnormal value, If it stops at a reasonable threshold, a qualified signal will be generated;

If the operation shutdown abnormal value exceeds the preset operation shutdown exception threshold, the previous adjacent operation will be marked as an auxiliary operation, and the traceback will continue until the operation shutdown abnormal value of the corresponding operation does not exceed the preset operation shutdown exception threshold. Accordingly, Obtain one or more groups of auxiliary runs; sum the duration performance value of this run and the duration performance values of all auxiliary runs to obtain the total duration value, and sum up all run interval durations to obtain the total time value. , numerically calculate the total value of the duration and the total time to obtain the luck coefficient; numerically compare the luck coefficient with the preset luck coefficient threshold, if the luck coefficient exceeds the preset luck coefficient threshold, generate a bad luck Qualified signal. If the timing coefficient does not exceed the preset timing coefficient threshold, a timing qualified signal is generated.

Further, the intelligent hardware module will store the test information, operation monitoring information and operation time detection information corresponding to the medical equipment. The intelligent hardware module also includes a storage performance detection module. The processor is communicated with the storage performance detection module. In the medical equipment During the communication test and operation process, the storage performance detection module detects and analyzes the storage process of the corresponding information, and uses this to determine the performance of the corresponding storage process. The specific analysis process is as follows:

Obtain the number of pauses of the storage process per unit time, sum up the duration of each storage pause to calculate the total pause duration, and obtain the average storage rate and actual storage duration per unit time, and combine the average storage rate and actual storage time. Duration, number of pauses and total pause duration are normalized and calculated to obtain the status data; the status data is numerically compared with the preset status data threshold. If the status data exceeds the preset status data threshold, storage performance is generated. Abnormal signal, if the storage data does not exceed the preset storage data threshold, a normal storage performance signal is generated; the storage performance abnormal signal is sent to the early warning management module through the processor.

Further, after the storage performance abnormality signal is generated, the current time is used as the end point of time to trace forward and the storage traceability period is set, and the number of generation times of the storage performance abnormality signal and the number of generation times of the storage performance normal signal within the storage traceability period are collected. Calculate the ratio between the number of generation times of abnormal storage performance signals and the number of generation times of normal storage performance signals to obtain the cycle memory ratio; and collect the temperature and humidity of the environment where the storage components in the intelligent hardware module are located, and compare the temperature and the preset appropriate Calculate the temperature difference and take the absolute value to obtain the temperature difference value. In the same way, the humidity difference value is obtained. The temperature difference value and the humidity difference value are numerically calculated to obtain the ring difference value;

Compare the ring difference value with the preset ring difference threshold. If the ring difference value exceeds the preset ring difference threshold, it is judged that the environment is unqualified; the length of time the storage component has been in the environmental unqualified state during the historical operation process is obtained, and the current date is Calculate the time difference with the preset life date of the storage component to obtain the end-of-life difference; normalize the period-to-deposit ratio, the end-of-life difference and the length of time in an environmentally unqualified state to obtain the risk-in-risk value. Compare the value with the preset insurance threshold. If the insurance value exceeds the preset insurance threshold, a backup elimination signal is generated; the backup elimination signal is sent to the early warning management module through the processor.

Compared with the prior art, the beneficial effects of the present invention are:

1. In the present invention, several groups of equipment components of medical equipment are monitored and analyzed through a multi-component joint monitoring module to achieve synchronous monitoring and accurate analysis of several equipment components of medical equipment, so that managers can grasp the operating safety status of several equipment components. , reduce the operation risk of medical equipment and increase its service life, and when all equipment components of the corresponding medical equipment are operating normally, the corresponding medical equipment will be inspected and analyzed through the operation time detection and judgment module, so that managers can have a detailed understanding of the status of the medical equipment. Run time risk information and promptly suspend the operation of medical equipment, thus helping to further reduce the operation risk of medical equipment and increase the service life of medical equipment;

2. In the present invention, the operation of the medical equipment is monitored through the intelligent hardware module, and data communication is established between the intelligent hardware module and the corresponding medical equipment. Data communication testing is performed before operation to ensure that the data communication is qualified during the operation of the medical equipment. It can ensure communication stability and ensure normal data transmission; and during the communication test and operation of medical equipment, the storage performance detection module detects and analyzes the storage process of the corresponding information, based on which it can judge the performance of the corresponding storage process and generate Storage risk prediction is performed after abnormal storage performance signals, so that managers can conduct timely investigations and take corresponding improvement measures to ensure storage efficiency and storage stability, as well as data security.

Description of the drawings

In order to facilitate understanding by those skilled in the art, the present invention will be further described below in conjunction with the accompanying drawings;

Figure 1 is an overall system block diagram of the present invention;

Figure 2 is a system block diagram of the intelligent hardware module in the present invention;

Figure 3 is a system block diagram of Embodiment 2 and Embodiment 3 of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

Embodiment 1: As shown in Figure 1-2, the present invention proposes a medical equipment management system based on the Internet of Things, including an intelligent hardware module. The intelligent hardware module monitors the operation of the medical equipment and uses the intelligent hardware module to Establish data communication with medical equipment and conduct two-way data transmission through the Internet of Things; among them, the intelligent hardware module includes a processor, data communication test module, multi-component joint monitoring module, operation time detection and judgment module, and early warning management module; in corresponding medical equipment Before starting operation, the data communication test module performs a response test on the data communication between the intelligent hardware module and the corresponding medical equipment, and performs a communication stress test when the response test is qualified to evaluate the stability and communication performance of the data communication, and generate When responding to a failed test signal or a failed stress test signal, it is sent to the early warning management module through the processor. When the early warning management module receives a failed response signal or a failed pressure test signal, it issues a corresponding early warning so that managers can grasp the data in detail. Communication status, and take corresponding improvement measures in a timely manner when the data communication status is not good, so as to ensure that the data communication is qualified during the operation of the medical equipment; after the communication test is completed and the response test is judged to be qualified and the communication stress test is qualified, the corresponding medical equipment starts to operate; The specific test process of the data communication test module is as follows:

Send data requests to the corresponding medical equipment through the data sender. Different data requests can be sent according to a predetermined pattern, such as continuous data requests, random data requests, etc. After sending the data request, wait for the corresponding medical equipment to respond. The response can be Immediate or delayed, it can be judged within the expected time; receive the response data of the corresponding medical device through the receiver, and judge whether the response of the corresponding medical device is normal based on the expected response mode and response time. If the corresponding medical device can If the response data is returned within the expected response time, and the data format and content of the response data are correct, then the data transmission is judged to be normal and a response test pass signal is generated. Otherwise, the data transmission is judged to be abnormal and a response test failure signal is generated. This can be performed once or as needed. Multiple response tests; analyze based on test results to identify potential problems and bottlenecks, and take corresponding optimization measures, such as adjusting communication parameters, repairing errors, etc., to improve the reliability of data transmission;

When judging that data transmission is normal, use a data generator to generate a large amount of test data. Test data of different types and sizes can be generated according to predetermined patterns, such as continuous data flow, random data flow, etc., and different data transmission loads are simulated through the simulator. Conditions, adjusted and optimized according to expected load conditions to simulate the data transmission load in actual usage scenarios; conduct communication stress testing under the generated test data and imposed load conditions, and record the time, rate and stability indicators of data transmission , compare the recorded test results with the corresponding expected results to judge the communication performance and stability performance. When the communication performance and stability performance are judged to be normal, a stress test qualified signal is generated, otherwise a stress test unqualified signal is generated, which is helpful Identify potential problems and bottlenecks and take corresponding optimization measures, such as adjusting communication parameters, improving data transmission rates, optimizing hardware resource allocation, etc. Through continuous optimization and improvement of the data communication system, its performance and stability can be improved, To cope with large data transmission needs in actual usage scenarios.

During the operation of medical equipment, the multi-component joint monitoring module collects the equipment components that need to be monitored in the corresponding medical equipment, and marks the corresponding equipment components as monitoring objects i, i={1,2,...,n}, n represents The number of equipment components to be monitored and n is a positive integer greater than 1; and the monitoring object i is subjected to operation detection and analysis, based on which it is judged whether the monitoring object i is running abnormally, and a monitoring early warning signal is generated when the monitoring object i is judged to be running abnormally, And the monitoring and early warning signals are sent to the early warning management module through the processor. The early warning management module issues corresponding early warnings after receiving the monitoring and early warning signals, achieving synchronous monitoring and accurate analysis of several equipment components of medical equipment, so that managers can grasp the status of several equipment components. Operation safety status, and timely suspension of the operation of medical equipment as needed or timely diagnosis of abnormalities in corresponding equipment components will help reduce the operational risks of medical equipment and increase its service life; the specific analysis process is as follows:

The pre-set monitoring parameters of the monitoring object i (including temperature, vibration frequency, amplitude and other parameters) and the parameter data requirements of the corresponding monitoring parameters are retrieved from the processor. The types of monitoring parameters of different equipment components will be different. The detection parameters are obtained The real-time parameter data of each monitoring parameter corresponding to the time point monitoring object i is compared with the corresponding parameter data requirements. If all real-time parameter data meet the parameter data requirements, the monitoring time point monitoring object i is judged to be running qualified. ; Otherwise, the corresponding parameter data that does not meet the parameter data requirements will be marked as unqualified parameter data, and the deviation value of the unqualified parameter data compared with the corresponding parameter data requirements will be marked as an unqualified difference value;

The preset deviation risk value corresponding to the monitoring parameter is retrieved from the processor. It should be noted that the greater the damage caused by the deviation of the monitoring parameter to the corresponding component, the greater the value corresponding to the preset deviation risk value. And the values of all preset deviation risk values are positive numbers; multiply the unqualified difference value and the corresponding preset deviation risk value to calculate the risk value, and sum up all the risk values of the monitoring object i at the detection time point Calculate the total insurance value; among them, the larger the value of the total insurance value, the greater the operational risk that exists at the corresponding moment for the corresponding monitoring object i; compare the total insurance value with the preset total insurance value threshold of the monitoring object i Numerical comparison is performed. If the total insurance value exceeds the preset total insurance value threshold, the operation of the monitoring object i at the detection time point is judged to be unqualified. If the total insurance value does not exceed the preset total insurance value threshold, the detection time point is determined. Monitoring object i runs qualified;

After judging whether the operation of the monitoring object i at the detection time point is qualified or unqualified, the detection time point corresponding to the unqualified operation of the monitoring object i within the unit time is marked as the analysis time point; the time interval between two adjacent groups of analysis time points is marked In order to analyze the interval length, the total insurance value at the analysis time point is subtracted from the preset total insurance value threshold to obtain the insurance excess value. All insurance excess values are summed and averaged to obtain the insurance excess value. Mean CFi, sum up all analysis intervals and take the average to obtain the time-sharing average FJi; use the formula YJi=et1*CFi+et2*FJi to numerically calculate the insurance excess average CFi and the time-sharing mean FJi to monitor The inspection value YJi of object i;

Among them, et1 and et2 are the preset weight coefficients, et1>et2>1; and, the value of the operation inspection value YJi is proportional to the insurance excess average CFi and the time-sharing average value FJi. The greater the value of the operation inspection value YJi Larger means that the greater the operational risk of the corresponding monitoring object i per unit time, the greater the possibility of anomalies, and the greater the need for management personnel to conduct timely investigation and diagnosis; compare the operation inspection value YJi with the preset operation inspection threshold of the monitoring object i By comparison, if the operation inspection value YJi exceeds the corresponding preset operation inspection threshold, it is judged that the monitoring object i is operating abnormally and a monitoring early warning signal is generated; if the operation inspection value YJi does not exceed the corresponding preset operation inspection threshold, the monitoring object i is judged to be operating normally.

If all the equipment components of the corresponding medical equipment are operating normally, the operation time detection and judgment module will perform operation time detection and analysis of the corresponding medical equipment, and accordingly generate a operation time qualified signal or operation time failure signal of the corresponding medical equipment, which will become the corresponding medical equipment. The unqualified signal during operation is sent to the early warning management module through the processor. When the early warning management module receives the unqualified signal during operation, it issues a corresponding early warning so that managers can have detailed information on the risk information of the running time of the medical equipment and timely suspend the medical equipment as needed. operation, thus helping to further reduce the operation risk of medical equipment and reduce the damage to medical equipment in a timely manner; the specific analysis process of operation time detection and analysis is as follows:

Obtain the current running start time of the corresponding medical equipment, calculate the time difference between the current time and the current running start time to obtain the current running time, and compare the current running time with the preset single running time threshold. If this time If the running time exceeds the preset single running time threshold, it indicates that the running time of the medical equipment exceeds the appropriate time limit, and continued operation will cause greater damage to it and the operation risk is greater, then an abnormal operation signal will be generated; if the running time is not If the preset single operation duration threshold is exceeded, the power curve of the current operation of the corresponding medical device is collected, a power rectangular coordinate system is established with time as the X-axis and operating power as the Y-axis, and the power curve is placed in the power rectangular coordinate system. ;

In the power rectangular coordinate system, draw the power determination ray parallel to the The operating power of the equipment is higher, which causes greater losses to the medical equipment; mark the time above the power determination ray as the risk duration, and use the formula SX=(cp1*YS+cp2*BY)/2 to compare the risk duration YS with This run time BY performs numerical calculations to obtain the duration performance value SX, in which cp1 and cp2 are preset weight coefficients, cp1>cp2>0; and, the larger the value of the duration performance value SX, the greater the need to stop the medical equipment in a timely manner. run;

The duration performance value SX is compared numerically with the preset duration performance threshold. If the duration performance value SX exceeds the preset duration performance threshold, an abnormal operation signal is generated; if the duration performance value SX does not exceed the preset duration performance threshold, a corresponding medical treatment will be generated. The time difference between the end time of the equipment's previous run and the start time of this run is calculated to obtain the run interval length, and the performance value of the last run time of the corresponding medical equipment is collected, and the time performance value is calculated as a ratio to the run interval length. Obtain the operation shutdown abnormal value. When the value of the operation shutdown abnormal value is large, it indicates that the rest time of the corresponding medical equipment after the end of the previous operation is obviously insufficient; the operation shutdown abnormal value is numerically compared with the preset operation shutdown exception threshold. If the operation shutdown abnormal value does not exceed the preset operation shutdown reasonable threshold, an operation time qualified signal is generated;

If the operation shutdown abnormal value exceeds the preset operation shutdown exception threshold, the previous adjacent operation will be marked as an auxiliary operation, and the traceback will continue until the operation shutdown abnormal value of the corresponding operation does not exceed the preset operation shutdown exception threshold. Accordingly, Obtain one or more groups of auxiliary runs; sum up the duration performance value of this run and all auxiliary run duration performance values to obtain the total duration value SY, and sum up all run interval durations to obtain the total time duration. Value JY, use the formula YX=fp1*SY/(fp2*JY+0.837) to numerically calculate the total duration value SY and the total time value JY to obtain the timing coefficient YX;

Among them, fp1 and fp2 are preset proportional coefficients, and the values of fp1 and fp2 are both positive numbers; and the value of the timing coefficient YX is directly proportional to the total duration value SY and inversely proportional to the total duration value JY ; The larger the value of the operation time coefficient YX is, the more timely it is necessary to stop the operation of the medical equipment; the operation time coefficient YX is compared with the preset operation time coefficient threshold. If the operation time coefficient YX exceeds the preset operation time coefficient threshold, Then a timing disqualification signal is generated. If the timing coefficient YX does not exceed the preset timing coefficient threshold, a timing qualified signal is generated.

Embodiment 2: As shown in Figure 3, the difference between this embodiment and Embodiment 1 is that the intelligent hardware module stores the test information, operation monitoring information and operation time detection information corresponding to the medical equipment. The intelligent hardware module also includes Storage performance detection module, the processor is communicatively connected with the storage performance detection module. During the communication test and operation process of the medical equipment, the storage performance detection module detects and analyzes the storage process of the corresponding information, and thereby determines the performance status of the corresponding storage process. , the specific analysis process is as follows:

Obtain the number of pauses of the storage process per unit time, sum up the duration of each storage pause to calculate the total pause duration, and obtain the average storage rate and actual storage duration per unit time, through the formula The average storage rate PC, the actual storage duration CS, the number of pauses TD and the total pause duration TS are normalized and calculated to obtain the storage data CK; among them, re1, re2, re3 and re4 are the preset proportion coefficients, re1, re2, The values of re3 and re4 are both greater than zero; and the larger the value of the storage status data CK is, the worse the storage status is; compare the storage status data CK with the preset storage status data threshold. If the storage status data CK exceeds the preset status data threshold, If the storage status data threshold is set, an abnormal storage performance signal is generated. If the storage status data CK does not exceed the preset storage status data threshold, a normal storage performance signal is generated; the storage performance abnormal signal is sent to the early warning management module through the processor, and the early warning management The module issues corresponding early warnings so that managers can conduct timely investigations and take corresponding improvement measures to ensure storage efficiency and storage stability.

Embodiment 3: The difference between this embodiment and Embodiment 1 and 2 is that after generating the storage performance abnormal signal, the current time is used as the end point of time to trace forward and set the storage traceback period. Preferably, the storage traceback period is 72h; the number of generation times of abnormal storage performance signals and the number of generation times of normal storage performance signals within the storage traceability period are collected, and the ratio of the number of generation times of abnormal storage performance signals and the number of generation times of normal storage performance signals is calculated to obtain the period storage difference ratio ZX; and collect the temperature and humidity of the environment where the storage component in the intelligent hardware module is located, and retrieve the preset suitable temperature and preset suitable humidity of the storage component from the processor, and the storage component is at the preset suitable temperature and preset suitable humidity. When the damage to its performance and life is small, calculate the difference between the temperature and the preset suitable temperature and take the absolute value to obtain the temperature difference value. In the same way, the humidity difference value is obtained;

The temperature difference value WQ and the humidity difference value SQ are numerically calculated using the formula HC=a1*WQ+a2*SQ to obtain the ring difference value HC; among them, a1 and a2 are preset weight coefficients, a1>a2>1; and, the ring difference value The value of HC is proportional to the temperature difference value WQ and the humidity difference value SQ. The larger the value of the ring difference value HC, the worse the corresponding environmental state and the greater the damage to the storage components; compare the ring difference value HC with The preset ring difference threshold is used for numerical comparison. If the ring difference value HC exceeds the preset ring difference threshold, the environment is judged to be unqualified;

Obtain the length of time HS that the storage component was in an environmentally unqualified state during the historical operation process, and calculate the time difference between the current date and the preset life date of the storage component to obtain the end-of-life difference SZ; use the formula The risk-to-risk value CX is calculated by normalizing the periodic savings ratio ZX, end-of-life difference value SZ and the time period HS in an environmentally unqualified state, where b1, b2, and b3 are preset proportional coefficients, b1>b3>b2 >0; Moreover, the larger the value of the risk value CX, the worse the performance of the storage component in the intelligent hardware module, and the greater the risk of use; compare the value of the risk value CX with the preset risk threshold, if the risk If the value CX exceeds the preset risk threshold, a backup elimination signal is generated; the backup elimination signal is sent to the early warning management module through the processor, and the early warning management module issues a corresponding early warning, and the management personnel promptly replaces the storage components in the intelligent hardware module, and Back up the corresponding storage information to play a storage risk prediction function and effectively avoid data information loss caused by storage hardware damage.

When the invention is in use, after the large-scale medical equipment is put into use, the operation monitoring is carried out through the intelligent hardware module, and the intelligent hardware module establishes data communication with the corresponding medical equipment, and performs data communication detection before the large-scale medical equipment is operated to ensure that the large-scale medical equipment is operated. During the operation of the equipment, the data communication is qualified, which can ensure communication stability and ensure normal data transmission; during the operation of large-scale medical equipment, several groups of equipment components corresponding to the medical equipment are monitored and analyzed through the multi-component joint monitoring module, so as to realize the monitoring and analysis of several groups of medical equipment. Synchronous monitoring and accurate analysis of individual equipment components, so that managers can understand the operating safety status of several equipment components, reduce the operational risks of medical equipment and extend its service life, and pass the operation time when all equipment components corresponding to the medical equipment are operating normally. The detection and judgment module will perform operation time detection and analysis on the corresponding medical equipment, so that managers can obtain detailed information about the operation time risk of medical equipment and promptly suspend the operation of medical equipment, thereby helping to further reduce the operation risk of medical equipment and increase the service life of medical equipment. .

The above formulas are all numerical calculations without dimensions. The formula is a formula obtained by collecting a large amount of data and conducting software simulation to obtain the latest real situation. The preset parameters in the formula are set by those skilled in the field according to the actual situation. The preferred embodiments of the invention disclosed above are only intended to help illustrate the invention. The preferred embodiments do not describe all details, nor do they limit the invention to specific implementations. Obviously, many modifications and variations are possible in light of the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can better understand and utilize the present invention. The invention is limited only by the claims and their full scope and equivalents.

Claims (8)

1. A medical equipment management system based on the Internet of Things, characterized by including an intelligent hardware module that monitors the operation of medical equipment and performs two-way data transmission through the Internet of Things; wherein, the intelligent hardware module includes Processor, data communication test module, multi-component joint monitoring module, operation time detection and judgment module and early warning management module; before the corresponding medical equipment starts running, the data communication test module responds to the data communication between the intelligent hardware module and the corresponding medical equipment Test, and when the response test passes, a communication stress test is performed to evaluate the stability and communication performance of data communication, and when a response test failure signal or a stress test failure signal is generated, it is sent to the early warning management module through the processor. , the early warning management module issues a corresponding early warning when it receives a failed response test signal or a failed stress test signal; after the communication test is completed and it is determined that the response test is qualified and the communication stress test is qualified, the corresponding medical equipment starts running; During the operation of medical equipment, the multi-component joint monitoring module collects the equipment components that need to be monitored in the corresponding medical equipment, and marks the corresponding equipment components as monitoring objects i, i={1,2,...,n}, n represents The number of equipment components to be monitored and n is a positive integer greater than 1; and the monitoring object i is subjected to operation detection and analysis, based on which it is judged whether the monitoring object i is running abnormally, and a monitoring early warning signal is generated when the monitoring object i is judged to be running abnormally, And the monitoring early warning signal is sent to the early warning management module through the processor, and the early warning management module issues a corresponding early warning after receiving the monitoring early warning signal; if all equipment components of the corresponding medical equipment are operating normally, the operation time detection and judgment module will operate the corresponding medical equipment. Real-time detection and analysis, based on which a qualified or unqualified signal for the corresponding medical equipment is generated. The unqualified signal for the corresponding medical equipment is sent to the early warning management module through the processor. The early warning management module receives the unqualified signal for the unqualified operation. A corresponding early warning is issued when a qualified signal is received. 2. A medical equipment management system based on the Internet of Things according to claim 1, characterized in that the specific test process of the data communication test module includes: Send a data request to the corresponding medical device through the data sender. After sending the data request, wait for the corresponding medical device to respond, receive the response data of the corresponding medical device through the receiver, and judge the corresponding medical device based on the expected response mode and response time. Whether the response is normal. If the corresponding medical device can return response data within the expected response time, and the data format and content of the response data are correct, then the data transmission is judged to be normal and a response test pass signal is generated. Otherwise, the data transmission is judged to be abnormal and a response test is generated. Failure signal; When judging that the data transmission is normal, use the data generator to generate a large amount of test data, simulate different data transmission load conditions through the simulator, and adjust and optimize according to the expected load conditions to simulate the data transmission load in actual usage scenarios; after generating Conduct a communication stress test under the test data and imposed load conditions, record the time, rate and stability indicators of data transmission, and compare the recorded test results with the corresponding expected results to judge the communication performance and stability performance. When the communication performance and stability are normal, a stress test passing signal is generated, otherwise a stress test failing signal is generated. 3. A medical equipment management system based on the Internet of Things according to claim 1, characterized in that the specific analysis process of running detection analysis is as follows: The processor retrieves the preset monitoring parameters of monitoring object i and the parameter data requirements of the corresponding monitoring parameters, obtains the real-time parameter data of each monitoring parameter corresponding to the monitoring object i at the detection time point, and compares the real-time parameter data with the corresponding Parameter data requirements are compared. If all real-time parameter data meet the parameter data requirements, it is judged that the monitoring object i at the detection time point is running qualified; otherwise, the corresponding parameter data that does not meet the parameter data requirements will be marked as unqualified parameter data and will be unqualified. The deviation value of parameter data compared with the corresponding parameter data requirements is marked as an unqualified difference value; The preset deviation risk value of the corresponding monitoring parameter that is set in advance is retrieved from the processor, the unqualified difference value is multiplied by the corresponding preset deviation risk value to calculate the risk value, and all parameters of the monitoring object i at the detection time point are calculated. The total insurance value is calculated by summing the risk values; the total insurance value is numerically compared with the preset total insurance value threshold of the monitoring object i. If the total insurance value exceeds the preset total insurance value threshold, the detection is judged The operation of the monitoring object i at the time point is unqualified. If the total insurance value does not exceed the preset total insurance value threshold, the monitoring object i at the time of detection is judged to be running qualified. 4. A medical equipment management system based on the Internet of Things according to claim 3, characterized in that, after judging whether the monitoring object i is running qualified or unqualified at the detection time point, the monitoring object i is running unqualified within the unit time. The corresponding detection time point is marked as the analysis time point; the time interval between two adjacent sets of analysis time points is marked as the analysis interval length, and the total insurance value at the analysis time point is subtracted from the preset total insurance value threshold to obtain the insurance value Excess value, sum and average all insurance excess values to obtain the insurance excess average, sum and calculate all analysis interval durations and average them to obtain the time-sharing average; calculate the insurance excess average Perform numerical calculation with the time-sharing average value to obtain the operation inspection value of the monitoring object i; numerically compare the operation inspection value with the preset operation inspection threshold of the monitoring object i. If the operation inspection value exceeds the preset operation inspection threshold, the operation inspection value of the monitoring object i is determined. Run abnormally and generate monitoring warning signals. 5. A medical equipment management system based on the Internet of Things according to claim 1, characterized in that the specific analysis process of operation time detection and analysis includes: Obtain the current running start time of the corresponding medical equipment, calculate the time difference between the current time and the current running start time to obtain the current running time, and compare the current running time with the preset single running time threshold. If this time If the running time exceeds the preset single running time threshold, an abnormal operation signal will be generated; if the running time does not exceed the preset single running time threshold, the power curve of the current running of the corresponding medical equipment will be collected, with time as Axis and operating power establish a power rectangular coordinate system for the Y-axis, and place the power curve into the power rectangular coordinate system; In the power rectangular coordinate system, draw the power determination ray parallel to the The risk duration and the current running duration are numerically calculated to obtain the duration performance value, and the duration performance value is numerically compared with the preset duration performance threshold. If the duration performance value exceeds the preset duration performance threshold, an operation time abnormal signal is generated. 6. A medical equipment management system based on the Internet of Things according to claim 5, characterized in that if the duration performance value does not exceed the preset duration performance threshold, the end time of the last operation of the corresponding medical device will be compared with the current one. The time difference is calculated at the start of each operation to obtain the operation interval duration, and the duration performance value of the adjacent previous operation of the corresponding medical equipment is collected. The duration performance value is compared with the operation interval duration to calculate the operation outage abnormal value, and the operation outage abnormal value is obtained. Compare the value with the preset abnormal operation threshold. If the abnormal value does not exceed the preset reasonable operation threshold, a qualified operation signal will be generated; If the operation shutdown abnormal value exceeds the preset operation shutdown exception threshold, the previous adjacent operation will be marked as an auxiliary operation, and the traceback will continue until the operation shutdown abnormal value of the corresponding operation does not exceed the preset operation shutdown exception threshold. Accordingly, Obtain one or more groups of auxiliary runs; sum the duration performance value of this run and the duration performance values of all auxiliary runs to obtain the total duration value, and sum up all run interval durations to obtain the total time value. , numerically calculate the total value of the duration and the total time to obtain the luck coefficient; numerically compare the luck coefficient with the preset luck coefficient threshold, if the luck coefficient exceeds the preset luck coefficient threshold, generate a bad luck Qualified signal. If the timing coefficient does not exceed the preset timing coefficient threshold, a timing qualified signal is generated. 7. A medical equipment management system based on the Internet of Things according to claim 1, characterized in that the intelligent hardware module stores test information, operation monitoring information and operation time detection information corresponding to the medical equipment, and the intelligent hardware module The group also includes a storage performance detection module. The processor is communicated with the storage performance detection module. During the communication test and operation process of the medical equipment, the storage performance detection module detects and analyzes the storage process of the corresponding information, and judges the corresponding storage process accordingly. The specific analysis process is as follows: Obtain the number of pauses of the storage process per unit time, sum up the duration of each storage pause to calculate the total pause duration, and obtain the average storage rate and actual storage duration per unit time, and combine the average storage rate and actual storage time. Duration, number of pauses and total pause duration are normalized and calculated to obtain the status data; the status data is numerically compared with the preset status data threshold. If the status data exceeds the preset status data threshold, storage performance is generated. Abnormal signal, if the storage data does not exceed the preset storage data threshold, a normal storage performance signal is generated; the storage performance abnormal signal is sent to the early warning management module through the processor. 8. A medical equipment management system based on the Internet of Things according to claim 7, characterized in that, after generating the storage performance abnormal signal, the current time is used as the end point of time to trace forward and set the storage traceability cycle, and collect To the number of times the abnormal storage performance signal is generated and the number of times the normal storage performance signal is generated within the storage traceability period, calculate the ratio between the number of times the abnormal storage performance signal is generated and the number of times the normal storage performance signal is generated to obtain the cycle-to-storage difference ratio; and collect For the temperature and humidity of the environment where the storage components in the intelligent hardware module are located, calculate the difference between the temperature and the preset suitable temperature and take the absolute value to obtain the temperature difference value. In the same way, the humidity difference value is obtained. Calculate the temperature difference value and the humidity difference value. Calculate the ring difference; Compare the ring difference value with the preset ring difference threshold. If the ring difference value exceeds the preset ring difference threshold, it is judged that the environment is unqualified; the length of time the storage component has been in the environmental unqualified state during the historical operation process is obtained, and the current date is Calculate the time difference with the preset life date of the storage component to obtain the end-of-life difference; normalize the period-to-deposit ratio, the end-of-life difference and the length of time in an environmentally unqualified state to obtain the risk-in-risk value. Compare the value with the preset insurance threshold. If the insurance value exceeds the preset insurance threshold, a backup elimination signal is generated; the backup elimination signal is sent to the early warning management module through the processor.