Medical equipment operation and maintenance management system based on information mining analysis

Abstract

The invention relates to the technical field of operation and maintenance management, in particular to a medical equipment operation and maintenance management system based on information mining analysis. According to the method, accurate screening of data abnormal features is introduced, so that the evaluation of the equipment operation state is more refined. According to the method, abnormal data can be effectively identified based on the mean offset of the data measured by the blood oxygen sensor, and the accuracy of equipment state evaluation is ensured in combination with the distribution trend of measurement errors. The influence of environmental factors on the equipment performance is deeply excavated, the dynamic change trend of the equipment performance under different environmental conditions can be identified by utilizing the correlation calculation of the environmental temperature change and the power consumption offset, and the sensitivity to the influence of external factors is improved. The evaluation mode of the operation state is more comprehensive, the subtle change trend of the equipment operation state can be accurately described through the collaborative analysis of the measured frequency fluctuation amplitude and the signal response time, and the accuracy of fault prediction is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of operation and maintenance management, and in particular to a medical equipment operation and maintenance management system based on information mining and analysis. Background Art

[0002] The field of operation and maintenance management technology includes the maintenance, monitoring and optimization management of equipment, systems and infrastructure to ensure their stable operation throughout the life cycle. This technical field involves equipment status monitoring, fault diagnosis, maintenance strategy optimization and maintenance plan formulation. With the development of information technology, operation and maintenance management has gradually introduced automated means to achieve real-time monitoring and remote control of equipment status through data collection, analysis and intelligent prediction.

[0003] Among them, the medical equipment operation and maintenance management system refers to a system that performs data monitoring, status evaluation and maintenance management for the operation and maintenance of medical equipment. The system obtains the operating parameters of medical equipment through data acquisition devices, including working status, usage time, temperature, humidity and performance indicators of key components, and evaluates the equipment status in combination with preset fault judgment criteria. The system uses an analysis method based on equipment operation data to identify possible hidden faults, and formulates a maintenance plan in combination with historical data and maintenance strategies.

[0004] The evaluation of the equipment operation status mainly relies on a single operation parameter, which cannot accurately describe the dynamic changes of the equipment status, resulting in some potential faults being difficult to detect in time. The screening method of abnormal data is relatively extensive, and only judges based on fixed thresholds. There is a lack of in-depth analysis of the data error distribution, which may ignore some key abnormal points. The impact of environmental factors has not been fully considered, and the performance changes of the equipment under different working environments are difficult to quantify, resulting in the possibility that the equipment may fail under specific environmental conditions and it is difficult to warn. The evaluation of the operation status lacks correlation analysis of multi-dimensional parameters, and indicators such as signal response time and measurement frequency fail to form effective coordination, unable to capture subtle changes in the equipment status, and reducing the ability to detect abnormal equipment operation. The identification method of the equipment degradation state is relatively simple, mainly relying on the changes of a single performance indicator, ignoring the cumulative trend of measurement errors and the decay of signal response time, resulting in insufficient ability to accurately evaluate the equipment degradation state. The operation and maintenance strategy lacks an intelligent optimization mechanism, the maintenance cycle is fixed, and it fails to make adjustments based on the actual operation of the equipment, which may lead to waste of maintenance resources or insufficient maintenance, thereby affecting the stable operation and service life of the equipment. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a medical equipment operation and maintenance management system based on information mining and analysis.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A medical equipment operation and maintenance management system based on information mining analysis comprises: The blood oxygen data evaluation module obtains the blood oxygen saturation measurement value of the blood oxygen sensor on the oximeter and calculates the corresponding mean offset, selects abnormal measurement data according to the offset, analyzes the distribution trend of the abnormal measurement data, and generates an abnormal feature set of blood oxygen data; The environmental impact analysis module obtains the power consumption and all ambient temperature data of the blood oxygen sensor based on the abnormal feature set of the blood oxygen data, calculates the correlation coefficient between the ambient temperature change and the power consumption offset, selects the environmental impact time period, and obtains the load environmental impact analysis result; The operation status evaluation module obtains the measurement frequency and signal response time of the blood oxygen sensor at the load environment impact time point based on the load environment impact analysis result, evaluates the deviation degree of the sensor operation status, and generates the sensor operation status analysis result; The decay stage identification module analyzes the cumulative decay change trend of the blood oxygen sensor measurement error based on the sensor operation status analysis result, and generates blood oxygen sensor decay information; The operation and maintenance strategy optimization module performs optimization and adjustment according to the blood oxygen sensor degradation information and the current operation and maintenance cycle to generate the blood oxygen sensor operation and maintenance management results.

[0007] As a further solution of the present invention, the abnormal feature set of blood oxygen data includes measurement error variance distribution, abnormal measurement data proportion, and abnormal measurement data trend; the load environment impact analysis results include blood oxygen sensor power consumption changes, ambient temperature gradient impact, and power consumption offset related parameters; the sensor operation status analysis results include measurement frequency fluctuation amplitude, signal response time offset, and operation status offset degree; the blood oxygen sensor decay information includes measurement error cumulative change trend, data loss rate, and signal response time change amplitude; the blood oxygen sensor operation and maintenance management results include maintenance adjustment requirements, operation and maintenance cycle optimization, and fault prevention rate analysis.

[0008] As a further solution of the present invention, the blood oxygen data evaluation module includes: The blood oxygen offset screening submodule obtains the blood oxygen saturation measurement value of the blood oxygen sensor on the desktop oximeter, calculates the mean offset of the blood oxygen saturation measurement value within a specified time, screens the time period in which the offset exceeds a preset offset threshold, calculates the offset rate within the screened time period, and obtains an offset time period set; The measurement error analysis submodule adopts the formula based on the offset time period set: ; Calculate measurement error variance , screen the error variance that exceeds the stability threshold to form the first abnormal interval; Where N is the number of measurements within the abnormal interval, is the ith measurement value in the abnormal interval, M is the number of measurement points used to calculate the variance of the measurement error in the abnormal interval, are the j measurements used to calculate the variance of the measurement error within the anomaly interval; The abnormal feature extraction submodule obtains the abnormal proportion of the measurement data in the first abnormal interval, analyzes the distribution trend of the abnormal measurement data, calculates the aggregation degree and distribution pattern of the abnormal values, obtains the distribution trend of the abnormal data, and establishes an abnormal feature set of the blood oxygen data.

[0009] As a further solution of the present invention, the environmental impact analysis module includes: The power consumption mean change analysis submodule obtains the power consumption data and all ambient temperature data of the blood oxygen sensor, selects the time period corresponding to the first abnormal interval in the blood oxygen data abnormal feature set, and extracts the power consumption measurement value of the blood oxygen sensor in the time period to obtain the power consumption mean change; The load and environment association calculation submodule selects the time period in which the mean change exceeds the load change threshold based on the mean change of power consumption to form a second abnormal interval, using the formula: ; Calculate the correlation coefficient r between ambient temperature change and power consumption offset; in, Represents the second abnormal interval The ambient temperature value at a time point, Represents the second abnormal interval The power consumption value of the blood oxygen sensor at a time point, Represents the average value of all ambient temperature values ​​within the time period, Represents the average of all power consumption values ​​within the time period, Represents the number of data points in the second anomaly interval; The load environment impact analysis submodule selects parameters whose correlation coefficients exceed a preset correlation threshold based on the correlation coefficient between the ambient temperature change and the power consumption offset, and marks the corresponding time period to obtain the load environment impact analysis result.

[0010] As a further solution of the present invention, the operating status evaluation module includes: The measurement parameter extraction submodule obtains the measurement frequency and signal response time of the blood oxygen sensor at the load environment impact time point based on the load environment impact analysis result, and records the timestamp corresponding to the data to form a measurement parameter set; The measurement frequency fluctuation calculation submodule adopts the formula based on the measurement parameter set: ; Calculate the measurement frequency fluctuation amplitude ; in, Representative The measurement frequency at each measurement time point, Represents the highest measurement frequency value within the measurement period. Represents the lowest measurement frequency value within the measurement period; The offset trend analysis submodule analyzes the offset trend between the change of the measured frequency fluctuation amplitude and the change of the signal response time based on the measured frequency fluctuation amplitude, using the formula: ; Calculate the offset parameter B of the blood oxygen sensor operating state, evaluate the offset degree of the sensor operating state, and generate the sensor operating state analysis result; in, Representative The measured frequency fluctuation amplitude at each measurement time point, Represents the average value of all measured frequency fluctuations within the time period, Representative The signal response time at each measurement time point, Represents the mean of all signal response times within a time period, Represents the number of data points in the time period.

[0011] As a further solution of the present invention, the decay stage identification module includes: The measurement error evaluation submodule obtains the measurement error accumulation of the blood oxygen sensor at the time points corresponding to the first abnormal interval and the second abnormal interval based on the analysis result of the sensor operation state, calculates the total measurement error accumulation at each time point, obtains the measurement error value of the data loss rate, and obtains the measurement error accumulation by statistically analyzing the distribution characteristics of the measurement error at each time point; The error accumulation analysis submodule analyzes the cumulative change trend of the measurement error accumulation amount, calculates the change amplitude of the blood oxygen sensor signal response time, evaluates the overall decay state by combining the error change trend and the signal response change, and generates blood oxygen sensor decay information.

[0012] As a further solution of the present invention, the operation and maintenance strategy optimization module includes: The maintenance record acquisition submodule acquires the maintenance record of the blood oxygen sensor at the decay characteristic time point based on the decay information of the blood oxygen sensor, determines the maintenance method at the corresponding time point, extracts the maintenance execution time, calculates the maintenance interval time, and acquires the current operation and maintenance cycle by calculating the interval sequence of multiple maintenance time points; The operation and maintenance cycle adjustment submodule calls the current operation and maintenance cycle, refers to the fault prevention rate of the maintenance method, analyzes the impact of multiple cycle adjustments on the operation and maintenance stability, evaluates the necessity of the current cycle adjustment, optimizes the operation and maintenance management, and generates the blood oxygen sensor operation and maintenance management results.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by introducing accurate screening of data anomaly features, the evaluation of the equipment operation status is made more refined. Based on the mean offset of the blood oxygen sensor measurement data, abnormal data can be effectively identified, and combined with the distribution trend of measurement errors, the accuracy of equipment status evaluation can be ensured. The impact of environmental factors on equipment performance has been deeply explored. By using the correlation calculation of ambient temperature changes and power consumption offsets, the dynamic change trend of equipment performance under different environmental conditions can be identified, and the sensitivity to the influence of external factors can be improved. The evaluation method of the operating status is more comprehensive. By measuring the collaborative analysis of the frequency fluctuation amplitude and the signal response time, the subtle change trend of the equipment operation status can be accurately portrayed, and the accuracy of fault prediction can be improved. The identification of the equipment decline stage no longer relies on a single indicator, but combines the cumulative change of measurement errors and the fluctuation of signal response time to form multi-dimensional decline information, accurately portraying the evolution process of the equipment health status. Based on the intelligent optimization strategy of the operation and maintenance cycle, the maintenance plan is more scientific and reasonable. Through the calculation of the maintenance time interval and the comparative analysis of the fault prevention rate, the maintenance rhythm can be dynamically adjusted to achieve the reasonable allocation of resources and maximize the life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a system flow chart of the present invention; Figure 2 is a flow chart of the blood oxygen data evaluation module of the present invention; Figure 3 It is a flow chart of the environmental impact analysis module of the present invention; Figure 4 is a flow chart of the operating status evaluation module of the present invention; Figure 5 is a flow chart of a decline stage identification module of the present invention; Figure 6 This is a flow chart of the operation and maintenance strategy optimization module of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0017] See also Figure 1 , a medical equipment operation and maintenance management system based on information mining and analysis includes: The blood oxygen data evaluation module obtains the blood oxygen saturation measurement value of the blood oxygen sensor on the desktop oximeter, calculates the mean offset of the blood oxygen saturation measurement value within a specified time, screens the time period in which the offset exceeds the preset offset threshold, calculates the measurement error variance at the selected time point, screens the first abnormal interval in which the measurement error variance exceeds the stability threshold and obtains the abnormal proportion of the measurement data in the first abnormal interval, analyzes the distribution trend of the abnormal measurement data, and generates an abnormal feature set of blood oxygen data; The environmental impact analysis module obtains the power consumption and all ambient temperature data of the blood oxygen sensor, calculates the mean change in the power consumption of the blood oxygen sensor in the time period corresponding to the first abnormal interval in the abnormal feature set of the blood oxygen data, iteratively selects the second abnormal interval whose mean change exceeds the load change threshold, calculates the correlation coefficient between the ambient temperature change and the power consumption offset according to the ambient temperature gradient in the second abnormal interval, selects the parameters whose correlation coefficient exceeds the preset correlation threshold and marks the corresponding time period, and obtains the load environment impact analysis result; Based on the load environment impact analysis results, the operation status evaluation module obtains the measurement frequency and signal response time of the blood oxygen sensor at the load environment impact time point, calculates the fluctuation amplitude of the measurement frequency, analyzes the deviation trend between the change of the measurement frequency fluctuation amplitude and the change of the signal response time, evaluates the deviation degree of the sensor operation status, and generates the sensor operation status analysis results; The decay stage identification module obtains the measurement error of the blood oxygen sensor at the corresponding time points of the first abnormal interval and the second abnormal interval based on the analysis result of the sensor operation state, and analyzes the cumulative change trend of the measurement error and the change amplitude of the blood oxygen sensor signal response time to generate the blood oxygen sensor decay information; The operation and maintenance strategy optimization module obtains the maintenance record of the blood oxygen sensor at the characteristic time point of the decline and the current operation and maintenance cycle based on the blood oxygen sensor decline information, and analyzes the adjustment requirements of the current operation and maintenance cycle and makes optimization adjustments with reference to the failure prevention rate of the maintenance method, and generates the blood oxygen sensor operation and maintenance management results; The abnormal feature set of blood oxygen data includes the measurement error variance distribution, the proportion of abnormal measurement data, and the trend of abnormal measurement data. The load environment impact analysis results include the power consumption change of the blood oxygen sensor, the impact of the ambient temperature gradient, and the power consumption offset related parameters. The sensor operation status analysis results include the measurement frequency fluctuation amplitude, the signal response time offset, and the degree of operation status offset. The blood oxygen sensor degradation information includes the cumulative change trend of the measurement error, the data loss rate, and the signal response time change amplitude. The blood oxygen sensor operation and maintenance management results include maintenance adjustment requirements, operation and maintenance cycle optimization, and fault prevention rate analysis.

[0018] See also Figure 2 , the blood oxygen data evaluation module includes:

[0019] The blood oxygen offset screening submodule obtains the blood oxygen saturation measurement value of the blood oxygen sensor on the desktop oximeter, calculates the mean offset of the blood oxygen saturation measurement value within a specified time, screens the time period in which the offset exceeds a preset offset threshold, calculates the offset rate within the screened time period, and obtains an offset time period set; To obtain the blood oxygen saturation measurement value of the blood oxygen sensor on the desktop oximeter, first collect blood oxygen saturation data in a continuous period of time through the blood oxygen sensor of the desktop oximeter. These data are recorded in seconds and stored in the data storage unit of the device. For example, within a 10-second monitoring time, the blood oxygen saturation value sequence collected by the device may be: , and then calculate the mean shift of these data. The mean shift is calculated by first finding the mean of the time period ,For example: , then calculate the absolute deviation of each measurement from the mean: Next, set a deviation threshold, such as 0.3%, and filter out time periods with deviations greater than the threshold. In this example, the time points with deviations greater than 0.3% are: 97.4% and 98.2%. Therefore, the corresponding time periods of these time points will be marked and eventually form a deviation time period set.

[0020] The measurement error analysis submodule is based on the offset time period set and uses the formula: ; Calculate measurement error variance , screen the error variance that exceeds the stability threshold to form the first abnormal interval; Where N is the number of measurements within the abnormal interval, is the ith measurement value in the abnormal interval, M is the number of measurement points used to calculate the variance of the measurement error in the abnormal interval, are the j measurements used in computing the variance of the measurement error within the anomaly interval.

[0021] Set N=3, M=3, the measured values ​​of the abnormal interval are 97.5%, 98.1%, and 97.8%, and the measured values ​​when calculating the mean are 97.4%, 97.9%, and 98.0%. Calculate the mean first: . Then calculate the variance components: , ; Then calculate the error variance: A stability threshold is set, for example, 0.05. When the calculated error variance is greater than the threshold, the time point is identified as abnormal, forming the first abnormal interval.

[0022] The abnormal feature extraction submodule obtains the abnormal proportion of the measurement data in the first abnormal interval, analyzes the distribution trend of the abnormal measurement data, calculates the aggregation degree and distribution pattern of the abnormal values, obtains the distribution trend of the abnormal data, and establishes an abnormal feature set of the blood oxygen data; Count the total number of all measured values ​​in this interval, recorded as The number of abnormal measurements is recorded as , abnormal proportion The calculation formula is as follows: , assuming that the first abnormal interval contains 5 measurement values, 2 of which exceed the set threshold, the abnormal proportion is calculated as follows: . Then, the distribution trend of abnormal measurement data is analyzed, focusing on whether the abnormal values ​​are temporally concentrated or random. The distribution trend of abnormal values ​​can be judged by statistically analyzing the relative position of abnormal data in the entire abnormal interval. For example, if the abnormal data is concentrated in the first 50% of the time period, there may be a trend of early signal abnormality; if the abnormal data is evenly distributed, it may be a random fluctuation abnormality. The sliding window method can be used to calculate the time interval between adjacent abnormal values ​​to obtain the time distribution trend of the abnormal values. If the abnormal data appears continuously in a short period of time, it indicates that the abnormal measurement values ​​are clustered; if the abnormal data interval is large, it indicates that the abnormal values ​​are randomly distributed. Finally, based on the distribution trend of abnormal data, an abnormal feature set of blood oxygen data is established for further subsequent analysis.

[0023] See also Figure 3 , the environmental impact analysis module includes: The power consumption mean change analysis submodule obtains the power consumption data and all ambient temperature data of the blood oxygen sensor, selects the time period corresponding to the first abnormal interval in the blood oxygen data abnormal feature set, and extracts the power consumption measurement value of the blood oxygen sensor in the time period to obtain the power consumption mean change; Obtain the power consumption data and all ambient temperature data of the blood oxygen sensor, and extract the power consumption and temperature data of all measurement time points from the stored data set. When the blood oxygen sensor works continuously, its power consumption will be affected by temperature and change. Assuming that the sensor collects data once a second and records and stores it in the device, in a 30-second period, the power consumption measurement data may be as follows . Then, the time period corresponding to the first abnormal interval in the blood oxygen data abnormal feature set is selected from these data, and the power consumption measurement value in this time period is extracted. For example, the first abnormal interval occurs in the time period 10s, 20s, then the power consumption data of this time period is taken Calculate the average power consumption during this time period. The average calculation uses the arithmetic mean of all data points. , which is calculated as follows: Then, calculate the mean change of the time period. The change is calculated as the difference between the mean of the time period and the mean of the previous time period. Assuming that the mean of the previous time period 0s and 10s is 2.2W, then the mean change is The calculation is as follows: Finally, the mean change of power consumption is obtained.

[0024] The load and environment correlation calculation submodule selects the time period when the mean change in power consumption exceeds the load change threshold based on the mean change in power consumption, and forms the second abnormal interval using the formula: ; Calculate the correlation coefficient r between ambient temperature change and power consumption offset; in, Represents the second abnormal interval The ambient temperature value at a time point, Represents the second abnormal interval The power consumption value of the blood oxygen sensor at a time point, Represents the average value of all ambient temperature values ​​within the time period, Represents the average of all power consumption values ​​within the time period, Represents the number of data points in the second anomaly interval.

[0025] Set the load change threshold to 0.15W. If the mean change in a certain time period is greater than the threshold, the time period is marked as the second abnormal interval. Therefore, this time period is identified as the second abnormal interval. Then, for the ambient temperature data in the second abnormal interval, the temperature gradient is calculated. The temperature gradient calculation method is the rate of change of temperature over time in this time period. Assume that the temperature data changes as follows in the second abnormal interval: Then, the correlation coefficient between the ambient temperature change and the power consumption offset is calculated, where , the temperature data is , power consumption data is , the mean temperature data is: , the average power consumption data is: 2.4W.

[0026] Calculate the numerator part: ; Calculate the denominator: ; Finally, the correlation coefficient is calculated: .

[0027] The load environment impact analysis submodule selects parameters whose correlation coefficients exceed a preset correlation threshold based on the correlation coefficient between ambient temperature change and power consumption offset, and marks the corresponding time period to obtain the load environment impact analysis results; Filter parameters whose correlation coefficients exceed the preset correlation threshold and mark the corresponding time period. Set the preset correlation threshold to 0.3, and the value range of the correlation coefficient r is ,in: It indicates a positive correlation, as the ambient temperature increases, the power consumption also increases; = ... , indicating that there is a weak correlation between the variables. Represents a moderate correlation, Represents a strong correlation. Therefore, in practical applications, a reasonable threshold can be set based on experience, such as 0.3 as the minimum significant correlation limit. Since the power consumption of the blood oxygen sensor is affected by the ambient temperature, but the influence may not be linear, it is necessary to adjust it in combination with experimental data. For example, in the experiment, the power consumption fluctuations of multiple sensors under different ambient temperatures are measured, and after the correlation coefficients of each time period are counted, it is analyzed at which threshold the correlation performance is stable and reflects the power consumption deviation trend. If the experimental data shows that when When , the power consumption change trend is consistent with the temperature change trend, and when When the power consumption change trend is relatively random, 0.3 can be set as the effective correlation threshold of the system. , it is considered that the ambient temperature has a correlation with the power consumption change, and the time period is marked. Since the calculated correlation coefficient r=0.36 is greater than 0.3, the time period is marked and the timestamp information is recorded. Finally, the load environment impact analysis results are obtained.

[0028] See also Figure 4 , the operation status assessment module includes: The measurement parameter extraction submodule obtains the measurement frequency and signal response time of the blood oxygen sensor at the load environment impact time point based on the load environment impact analysis results, and records the timestamp corresponding to the data to form a measurement parameter set; Extract the measurement frequency and signal response time of the blood oxygen sensor at the time point affected by the load environment. First, filter out all time periods affected by the load environment and sort them by timestamp to ensure data integrity. For example, at the time point ; The corresponding measurement frequency data may be: ; Signal response time data may be ; Then, the measurement parameters at all time points are aggregated to form a measurement parameter set for subsequent calculations.

[0029] The measurement frequency fluctuation calculation submodule is based on the measurement parameter set and uses the formula: ; Calculate the measurement frequency fluctuation amplitude ; in, Representative The measurement frequency at each measurement time point, Represents the highest measurement frequency value within the measurement period. Represents the lowest measurement frequency value during the measurement period.

[0030] The measured frequency data is , calculate the highest and lowest measured frequency values: . Substitute the formula to calculate the measured frequency fluctuation amplitude: .

[0031] The offset trend analysis submodule analyzes the offset trend between the change of the measured frequency fluctuation amplitude and the change of the signal response time based on the measured frequency fluctuation amplitude, using the formula: ; Calculate the offset parameter B of the blood oxygen sensor operating state, evaluate the offset degree of the sensor operating state, and generate the sensor operating state analysis result; in, Representative The measured frequency fluctuation amplitude at each measurement time point, Represents the average value of all measured frequency fluctuations within the time period, Representative The signal response time at each measurement time point, Represents the mean of all signal response times within a time period, Represents the number of data points in the time period, Represents the summation of all data points within the time period.

[0032] set up , measure the frequency fluctuation amplitude data ; Signal response time data . Calculate the mean: Calculate the mean volatility: ; Calculate the mean response time: ; Calculate the numerator part: .

[0033] The offset parameter B is used to measure the correlation between the measurement frequency fluctuation amplitude and the signal response time change. When the B value is small, it means that the change trend of the two is relatively stable and the degree of offset is low, that is, the operating state of the sensor is relatively stable. When the B value increases, it means that there is a strong offset trend between the change in the measurement frequency fluctuation amplitude and the change in the signal response time, which may mean that the sensor is abnormal or unstable under the influence of the load environment. In this example, B=0.00018 means that the relationship between the change in the measurement frequency fluctuation amplitude and the signal response time is small, and the degree of offset of the sensor operating state is low. If the B value continues to increase during subsequent monitoring, it can be used as an early warning signal of abnormal sensor operating status.

[0034] See also Figure 5 , the decay stage identification module includes: The measurement error evaluation submodule obtains the measurement error accumulation of the blood oxygen sensor at the corresponding time points in the first abnormal interval and the second abnormal interval based on the analysis results of the sensor operation status, calculates the total measurement error accumulation at each time point, and obtains the measurement error value of the data loss rate, and obtains the measurement error accumulation by statistically analyzing the distribution characteristics of the measurement error at each time point; First, select data at specific time points in the first abnormal interval and the second abnormal interval, and perform cumulative calculations on the measurement errors of the blood oxygen sensor at these time points. For example, suppose the measurement value of the sensor at a certain time point is , while the actual reference value is , then the measurement error at this time point is , calculate the time point error values ​​of all abnormal intervals in turn, and then obtain the overall measurement error accumulation. The calculation of data loss rate is based on the set sampling period. For example, if the sampling period is set to 1s, a total of 10 sets of data should be obtained within 10s. If 8 sets of data are actually obtained, the data loss rate L is calculated as follows: , when calculating the cumulative amount of measurement error, all error values ​​need to be accumulated and summed. For example, in the first abnormal interval, assuming that the error values ​​of the five time points are , then the cumulative measurement error in this interval is The calculation is as follows: , the accumulated measurement error can be obtained through the above calculation.

[0035] The error accumulation analysis submodule analyzes the cumulative change trend of the measurement error accumulation, calculates the change amplitude of the blood oxygen sensor signal response time, combines the error change trend with the signal response change to evaluate the overall decay state, and generates the blood oxygen sensor decay information; Call the measurement error accumulation, analyze the cumulative change trend of the error over time, and calculate the error growth rate, assuming that the time point interval is 1s; The corresponding error accumulation data is ; Then calculate the error change rate ,in, The accumulated measurement error at each time point, in %, indicates the accumulated measurement error of the sensor at the current moment. The accumulated measurement error at a time point is related to Comparisons are made to calculate the rate of change of the error. The timestamp of a time point, in seconds, indicates the acquisition time of the measurement data. The timestamp of the time point, and Calculate the time interval. For the above data, calculate the rate of change of the error between the first two points: , calculate the rate of each point in turn, and get the error change rate sequence: At the same time, the change amplitude of the signal response time is calculated, and the signal response time measurement value sequence is set to , calculate the signal response variation range , where S is the signal response time data sequence, which contains the measured values ​​of the signal response time at multiple time points. max (S): the maximum value of the signal response time, indicating the longest response time of the sensor at a certain time point. min (S): the minimum value of the signal response time, indicating the shortest response time of the sensor at a certain time point. The decay trend of the blood oxygen sensor can be identified by the error change rate and the signal response change amplitude. If the accumulated measurement error continues to grow, and the error growth rate remains in a high value range (for example, close to 2% / s), it indicates that the measurement stability of the sensor has decreased. At the same time, if the fluctuation range of the signal response time increases (for example, more than 0.3s), it means that the sensor's response delay to the input signal increases, further exacerbating the unpredictability of the measurement error. In the above case, it can be determined that the blood oxygen sensor has entered the decay stage, and its working state is no longer stable, which may affect the measurement reliability. Therefore, the blood oxygen sensor decay information is finally generated by combining the error accumulation change trend with the signal response change.

[0036] See also Figure 6 , the operation and maintenance strategy optimization module includes: The maintenance record acquisition submodule obtains the maintenance record of the blood oxygen sensor at the decay characteristic time point based on the blood oxygen sensor decay information, determines the maintenance method corresponding to the time point, extracts the maintenance execution time, calculates the maintenance interval time, and obtains the current operation and maintenance cycle by calculating the interval sequence of multiple maintenance time points; First, we need to screen out the decay characteristic time points, which are usually significantly related to the degradation of sensor performance, increase in measurement error, or change in signal response time. Assume that the normal measurement error range of the blood oxygen sensor is If the error value at a certain time point exceeds 2.5%, it can be determined that the time point is a potential decay feature point. For example, the measurement error at a certain time point is 3.1%, which is higher than the threshold, and further analysis of the maintenance record is required. Next, call the maintenance database to obtain the maintenance method at the corresponding time point. The maintenance method may include sensor calibration, component replacement, firmware upgrade, etc. The maintenance record stores information such as maintenance type, maintenance time, fault description, etc. For example, a sensor calibration was performed at 2024-01-15 08:00, and the measurement error recovered to 1.2% after maintenance. Extract the execution time of all maintenance operations and establish a time series, such as , calculate the interval between adjacent maintenance times to evaluate the current operation and maintenance cycle. For example, assuming that the intervals between two maintenances are 14 days, 21 days, and 10 days respectively, the current operation and maintenance cycle can be calculated based on the average 15 days, finally get the current operation and maintenance cycle.

[0037] The operation and maintenance cycle adjustment submodule calls the current operation and maintenance cycle, refers to the fault prevention rate of the maintenance method, analyzes the impact of multiple cycle adjustments on the operation and maintenance stability, evaluates the necessity of the current cycle adjustment, optimizes the operation and maintenance management, and generates the blood oxygen sensor operation and maintenance management results; Call the current operation and maintenance cycle to evaluate the fault prevention capabilities of different maintenance strategies. The fault prevention rate measures the effectiveness of the maintenance method and calculates the prevention capabilities under different operation and maintenance cycles. For example, for the relationship between the maintenance interval T and the fault occurrence rate F, the following can be used: Indicates the failure prevention rate under the current maintenance cycle, is the number of faults that occur in the current cycle, is the number of failures that occur during the baseline operation and maintenance cycle. If 10 failures occur within 15 days of the baseline cycle and 14 failures occur within 20 days of the current cycle, then the calculation is: , negative values ​​indicate that the number of failures in the current cycle increases and the maintenance interval needs to be shortened. Based on this, the impact of different adjustment strategies on operation and maintenance stability is analyzed, and finally the operation and maintenance management results of the blood oxygen sensor are generated.

[0038] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A medical equipment operation and maintenance management system based on information mining and analysis, characterized in that: The system comprises: The blood oxygen data evaluation module obtains the blood oxygen saturation measurement value of the blood oxygen sensor on the oximeter and calculates the corresponding mean offset, selects abnormal measurement data according to the offset, analyzes the distribution trend of the abnormal measurement data, and generates an abnormal feature set of blood oxygen data; The environmental impact analysis module obtains the power consumption and all ambient temperature data of the blood oxygen sensor based on the abnormal feature set of the blood oxygen data, calculates the correlation coefficient between the ambient temperature change and the power consumption offset, selects the environmental impact time period, and obtains the load environmental impact analysis result; The operation status evaluation module obtains the measurement frequency and signal response time of the blood oxygen sensor at the load environment impact time point based on the load environment impact analysis result, evaluates the deviation degree of the sensor operation status, and generates the sensor operation status analysis result; The decay stage identification module analyzes the cumulative decay change trend of the blood oxygen sensor measurement error based on the sensor operation status analysis result, and generates blood oxygen sensor decay information; The operation and maintenance strategy optimization module performs optimization and adjustment according to the blood oxygen sensor degradation information and the current operation and maintenance cycle to generate the blood oxygen sensor operation and maintenance management results.

2. The medical equipment operation and maintenance management system based on information mining analysis according to claim 1 is characterized in that: The abnormal feature set of blood oxygen data includes measurement error variance distribution, abnormal measurement data proportion, and abnormal measurement data trend. The load environment impact analysis result includes blood oxygen sensor power consumption change, ambient temperature gradient impact, and power consumption offset related parameters. The sensor operation status analysis result includes measurement frequency fluctuation amplitude, signal response time offset, and operation status offset degree. The blood oxygen sensor decay information includes measurement error cumulative change trend, data loss rate, and signal response time change amplitude. The blood oxygen sensor operation and maintenance management result includes maintenance adjustment requirements, operation and maintenance cycle optimization, and fault prevention rate analysis.

3. The medical equipment operation and maintenance management system based on information mining analysis according to claim 2 is characterized in that: The blood oxygen data evaluation module includes: The blood oxygen offset screening submodule obtains the blood oxygen saturation measurement value of the blood oxygen sensor on the desktop oximeter, calculates the mean offset of the blood oxygen saturation measurement value within a specified time, screens the time period in which the offset exceeds a preset offset threshold, calculates the offset rate within the screened time period, and obtains an offset time period set; The measurement error analysis submodule adopts the formula based on the offset time period set: ； Calculate measurement error variance , screen the error variance that exceeds the stability threshold to form the first abnormal interval; Where N is the number of measurements within the abnormal interval, is the ith measurement value in the abnormal interval, M is the number of measurement points used to calculate the variance of the measurement error in the abnormal interval, are the j measurements used to calculate the variance of the measurement error within the anomaly interval; The abnormal feature extraction submodule obtains the abnormal proportion of the measurement data in the first abnormal interval, analyzes the distribution trend of the abnormal measurement data, calculates the aggregation degree and distribution pattern of the abnormal values, obtains the distribution trend of the abnormal data, and establishes an abnormal feature set of the blood oxygen data.

4. The medical equipment operation and maintenance management system based on information mining analysis according to claim 3 is characterized in that: The environmental impact analysis module includes: The power consumption mean change analysis submodule obtains the power consumption data and all ambient temperature data of the blood oxygen sensor, selects the time period corresponding to the first abnormal interval in the blood oxygen data abnormal feature set, and extracts the power consumption measurement value of the blood oxygen sensor in the time period to obtain the power consumption mean change; The load and environment association calculation submodule selects the time period in which the mean change exceeds the load change threshold based on the mean change of power consumption to form a second abnormal interval, using the formula: ； Calculate the correlation coefficient r between ambient temperature change and power consumption offset; in, Represents the second abnormal interval The ambient temperature value at a time point, Represents the second abnormal interval The power consumption value of the blood oxygen sensor at a time point, Represents the average value of all ambient temperature values ​​within the time period, Represents the average of all power consumption values ​​within the time period, Represents the number of data points in the second anomaly interval; The load environment impact analysis submodule selects parameters whose correlation coefficients exceed a preset correlation threshold based on the correlation coefficient between the ambient temperature change and the power consumption offset, and marks the corresponding time period to obtain the load environment impact analysis result.

5. The medical equipment operation and maintenance management system based on information mining analysis according to claim 4 is characterized in that: The operating status assessment module comprises: The measurement parameter extraction submodule obtains the measurement frequency and signal response time of the blood oxygen sensor at the load environment impact time point based on the load environment impact analysis result, and records the timestamp corresponding to the data to form a measurement parameter set; The measurement frequency fluctuation calculation submodule adopts the formula based on the measurement parameter set: ； Calculate the measurement frequency fluctuation amplitude ; in, Representative The measurement frequency at each measurement time point, Represents the highest measurement frequency value within the measurement period. Represents the lowest measurement frequency value within the measurement period; The offset trend analysis submodule analyzes the offset trend between the change of the measured frequency fluctuation amplitude and the change of the signal response time based on the measured frequency fluctuation amplitude, using the formula: ； Calculate the offset parameter B of the blood oxygen sensor operating state, evaluate the offset degree of the sensor operating state, and generate the sensor operating state analysis result; in, Representative The measured frequency fluctuation amplitude at each measurement time point, Represents the average value of all measured frequency fluctuations within the time period, Representative The signal response time at each measurement time point, Represents the mean of all signal response times within a time period, Represents the number of data points in the time period.

6. The medical equipment operation and maintenance management system based on information mining analysis according to claim 5 is characterized in that: The decay stage identification module comprises: The measurement error evaluation submodule obtains the measurement error accumulation of the blood oxygen sensor at the time points corresponding to the first abnormal interval and the second abnormal interval based on the analysis result of the sensor operation state, calculates the total measurement error accumulation at each time point, obtains the measurement error value of the data loss rate, and obtains the measurement error accumulation by statistically analyzing the distribution characteristics of the measurement error at each time point; The error accumulation analysis submodule analyzes the cumulative change trend of the measurement error accumulation amount, calculates the change amplitude of the blood oxygen sensor signal response time, evaluates the overall decay state by combining the error change trend and the signal response change, and generates blood oxygen sensor decay information.

7. The medical equipment operation and maintenance management system based on information mining analysis according to claim 6 is characterized in that: The operation and maintenance strategy optimization module includes: The maintenance record acquisition submodule acquires the maintenance record of the blood oxygen sensor at the decay characteristic time point based on the decay information of the blood oxygen sensor, determines the maintenance method at the corresponding time point, extracts the maintenance execution time, calculates the maintenance interval time, and acquires the current operation and maintenance cycle by calculating the interval sequence of multiple maintenance time points; The operation and maintenance cycle adjustment submodule calls the current operation and maintenance cycle, refers to the fault prevention rate of the maintenance method, analyzes the impact of multiple cycle adjustments on the operation and maintenance stability, evaluates the necessity of the current cycle adjustment, optimizes the operation and maintenance management, and generates the blood oxygen sensor operation and maintenance management results.

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