Medical instrument inspection data processing system

Abstract

The invention relates to the technical field of data processing, in particular to a medical instrument inspection data processing system, which comprises an inspection data acquisition module used for acquiring production inspection information and ex-warehouse use information of medical instruments; the production data analysis module is used for analyzing batch qualification parameters, inspection integrity and sampling inspection operation characteristics; the sampling inspection storage and analysis module is used for analyzing batch sampling inspection characteristics; the ex-warehouse use analysis module is used for analyzing the use characteristics, the use integrity and the self-inspection information consistency of the instruments; the ex-warehouse storage analysis module is used for analyzing the user operation characteristics and analyzing the user levels according to the user operation characteristics; the feature comprehensive analysis module is used for analyzing the warehouse-out features of the instruments; and the data exception processing module is used for analyzing and outputting the exception of the ex-warehouse use information of the medical equipment. According to the invention, abnormity analysis of the medical instrument inspection data is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a medical device inspection data processing system. Background Art

[0002] Abnormal analysis of medical device inspection data is mainly based on strict control and in-depth analysis of data quality. With the widespread application of medical devices, data has become the key to evaluating their quality and safety. Abnormal analysis involves steps such as data preprocessing, feature extraction, and model building to ensure the accuracy and reliability of test results.

[0003] Chinese Patent Publication No.: CN105653730A discloses a data quality inspection method and device, including: receiving an inspection request; in response to the inspection request, obtaining an inspection queue, wherein the inspection queue has data to be inspected; obtaining the first stock data of the t-1th day and the second stock data of the t+1th day in the data warehouse, wherein the first stock data and the second stock data have different attributes, and t is a positive integer greater than or equal to 2; searching in the inspection queue whether there is data on the tth day corresponding to the attribute of the first stock data of the t-1th day, and obtaining a first inspection result; searching in the inspection queue whether there is data on the tth day corresponding to the attribute of the second stock data of the t+1th day, and obtaining a second inspection result; based on the first inspection result and the second inspection result, generating the inspection result of the data to be inspected. This invention only realizes the inspection and analysis of the attributes of data on different dates, and does not realize the abnormality analysis of the data generated in the operation of medical devices in combination with the sampling data after the production of the device and the operation and use of the user after the device is shipped out of the warehouse, and there are problems of low efficiency in the analysis of medical device inspection data and inaccurate analysis of the abnormality of medical device inspection data. Summary of the invention

[0004] An object of the present invention is to provide a medical device inspection data processing system to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions: A medical device inspection data processing system, comprising: Inspection data collection module, used to collect production inspection information and outbound use information of medical devices; Production data analysis module, used to analyze batch qualification parameters, inspection integrity and sampling operation characteristics based on production inspection information; The sampling inspection storage and analysis module is used to store production inspection information and sampling inspection operation characteristics, and analyze batch sampling inspection characteristics; The outbound usage analysis module is used to analyze the device usage characteristics and information verification parameters based on the outbound usage information and production inspection information; The outbound storage analysis module stores outbound usage information and equipment usage characteristics, and analyzes user levels; The feature comprehensive analysis module is used to analyze the device delivery characteristics according to the batch sampling characteristics, device usage characteristics and batch qualified parameters, and is also used to analyze the user operation parameters according to the user level, delivery usage information and device usage characteristics, and to process the analysis process of the device delivery characteristics, and is also used to process the analysis process of the user operation parameters according to the information verification parameters; The data anomaly processing module is used to analyze and output the anomaly of the medical device's outbound usage information based on the device's outbound characteristics.

[0006] Furthermore, the production data analysis module is provided with a qualified parameter analysis unit, which is used to count the number of medical devices with qualified sampling status as the qualified sampling number, and analyze the batch qualified parameters according to the qualified sampling number NA1(p), production batch p, batch number NA3(p) and sampling number NA2(p) to obtain the batch qualified parameter A(p).

[0007] Furthermore, the production data analysis module is also provided with an inspection integrity analysis unit, which is used to count the number of data with missing values ​​in the random inspection self-inspection information as the random inspection missing number NL1(p,i), count the number of data with abnormal values ​​in the random inspection self-inspection information as the random inspection abnormal number NL2(p,i), and judge the inspection integrity based on the random inspection missing number and the random inspection abnormal number, and the inspection integrity includes complete data and incomplete data.

[0008] Furthermore, the production data analysis module is also provided with a sampling feature analysis unit, which is used to analyze the sampling operation characteristics according to the sampling operation power P1 (p, i, t) and the equipment rated power Pe to obtain the sampling operation characteristics C (p, i).

[0009] Furthermore, the sampling storage and analysis module classifies medical devices with a qualified sampling status and incomplete inspection integrity data as Class I devices, medical devices with a qualified sampling status and complete inspection integrity data as Class II devices, and medical devices with an unqualified sampling status as Class III devices, and analyzes batch sampling characteristics based on Class I devices, Class II devices, Class III devices and sampling operation characteristics to obtain batch sampling characteristics B(p).

[0010] Furthermore, the outbound storage analysis module analyzes the user operation characteristics according to the usage records and the device usage characteristics to obtain the user operation characteristics as R(n); The outbound storage analysis module analyzes the user level according to the user operation feature R(n) to obtain the user level, which includes the first level and the second level.

[0011] Furthermore, the feature comprehensive analysis module is provided with a warehouse-out feature analysis unit, which is used to analyze the equipment warehouse-out feature according to the batch sampling feature B(p), the equipment usage feature D(p,i,t) and the batch qualified parameter A(p) to obtain the equipment warehouse-out feature F(p,i).

[0012] Furthermore, the feature comprehensive analysis module is also provided with a user level analysis unit, which is used to analyze the user operation parameters according to the user level, usage records and device usage characteristics to obtain the user operation parameters G(p,i); The feature comprehensive analysis module is also provided with an operation association analysis unit, which is used to analyze the operation difference state according to the user operation parameters to obtain the operation difference state, wherein the operation difference state includes a large difference and a small difference, and when the operation difference state is a large difference, the analysis process of the equipment outbound feature is processed, and the processed equipment outbound feature is F1(p,i).

[0013] Furthermore, the feature comprehensive analysis module is also provided with a self-check correlation analysis unit, which is used to analyze data security based on usage integrity and self-check information consistency. The data security includes data security and data insecurity. When the data is insecure, the analysis process of the user operation parameters is processed, and the processed user operation parameters are G1(p,i).

[0014] Furthermore, the data anomaly processing module analyzes the abnormality of the medical device's outbound usage information according to the device outbound characteristics to obtain the abnormality of the outbound usage information, wherein the abnormality of the outbound usage information includes normal data and abnormal data, and when the abnormality of the outbound usage information is data abnormality, the abnormality of the outbound usage information is output to the user.

[0015] The beneficial effects of the present invention are as follows: through the collection of production inspection data and outbound use information of medical devices by the inspection data collection module, and the analysis of the collected data by other modules, the abnormality analysis of medical device inspection data can be realized by integrating the sampling data after the production of medical devices and the user operation data and operation data after the medical devices are outbound and used, and judging from multiple angles whether the data generated during the operation of the medical devices is abnormal, ensuring the stable operation of the medical devices, thereby improving the system's analysis efficiency of medical device inspection data and improving the accuracy of abnormality analysis of medical device inspection data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 Schematic diagram of the structure of the medical device inspection data processing system of this embodiment.

[0018] Figure 2 This is a schematic diagram of the structure of the production data analysis module of this embodiment.

[0019] Figure 3 This is a schematic diagram of the structure of the outbound usage analysis module in this embodiment.

[0020] Figure 4 Schematic diagram of the structure of the feature comprehensive analysis module of this embodiment. DETAILED DESCRIPTION

[0021] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0022] See also Figure 1 As shown, it is a medical device inspection data processing system of this embodiment, including: The inspection data collection module is used to collect production inspection information and outbound usage information of medical devices, the production inspection information includes production batch, batch quantity, sampling quantity and sampling device information, the sampling device information includes sampling status, sampling operation information and sampling self-inspection information, the sampling self-inspection information includes self-inspection content and sampling information type, the self-inspection content includes but is not limited to data generated during the operation of the medical device, such as device temperature, device amplitude and output frequency, the sampling status includes qualified and unqualified, the sampling operation information includes sampling operation power and device rated power, the outbound usage information includes usage record, outbound operating power and usage self-inspection information, the usage self-inspection information includes self-inspection content and usage information type, the production inspection information is collected by user interactive input, and the outbound usage information is collected by importing through the medical device operation data management platform.

[0023] Specifically, this embodiment is applied to the cloud side of the medical device management data platform to analyze and store the collected inspection data of the medical devices, and to detect abnormal data to ensure the normal operation of the medical devices.

[0024] Please continue reading Figure 1 As shown, the medical device inspection data processing system also includes: The production data analysis module is used to analyze batch qualification parameters, inspection integrity and sampling operation characteristics according to production inspection information. The production data analysis module is connected to the inspection data acquisition module.

[0025] See also Figure 2 As shown, the production data analysis module includes: The qualified parameter analysis unit is used to analyze the batch qualified parameters based on the production inspection information.

[0026] Specifically, the qualified parameter analysis unit described in this embodiment counts the number of medical devices with qualified sampling status as the qualified sampling number, and analyzes the batch qualified parameters according to the qualified sampling number, production batch, batch number and sampling number, and sets the batch qualified parameter to A(p), and sets A(p)=NA1(p) / NA2(p)-[1-NA1(p)] / NA3(p); wherein NA1(p) represents the qualified sampling number, NA2(p) represents the sampling number, NA3(p) represents the batch number, and p represents the production batch.

[0027] Specifically, in this embodiment, the qualified parameter analysis unit analyzes the number of qualified random inspections, production batches, batch quantities and random inspection quantities to analyze the batch qualified parameters, and uses the batch qualified parameters to represent the characteristic relationship between qualified devices, unqualified devices and random inspection quantities during the random inspection of medical devices, so that the batch qualified parameters are related to the device qualification rate and the number of unqualified products is related to the batch quantity, thereby improving the system's analysis efficiency of medical device inspection data and improving the accuracy of abnormality analysis of medical device inspection data.

[0028] Please continue reading Figure 2 As shown, the production data analysis module also includes: The inspection integrity analysis unit is used to analyze the inspection integrity according to the production inspection information, and the inspection integrity analysis unit is connected to the qualified parameter analysis unit.

[0029] Specifically, the inspection integrity analysis unit described in this embodiment determines whether the data in the random sampling and self-inspection information are missing values ​​and abnormal values. If the data content in the random sampling and self-inspection information does not exist, the inspection integrity analysis unit determines that the data in the current analysis random sampling and self-inspection information is a missing value. If the value of the data in the random sampling and self-inspection information is not within the system preset value range, the inspection integrity analysis unit determines that the data in the current analysis random sampling and self-inspection information is an abnormal value.

[0030] Specifically, the inspection integrity analysis unit in this embodiment counts the number of data with missing values ​​in the random inspection self-inspection information as the random inspection missing number, counts the number of data with abnormal values ​​in the random inspection self-inspection information as the random inspection abnormal number, and analyzes the inspection integrity according to the random inspection missing number and the random inspection abnormal number. If [NL1(p,i)+NL2(p,i)] / NL3(p)>α, the inspection integrity analysis unit determines that the inspection integrity is complete data; if [NL1(p,i)+NL2(p,i)] / NL3(p)≤α, the inspection integrity analysis unit determines that the inspection integrity is incomplete data; wherein NL1(p,i) represents the random inspection missing number, NL2(p,i) represents the random inspection abnormal number, NL3(p) represents the number of data in the random inspection self-inspection information, i represents the random inspection device number, i∈N + , α represents the integrity threshold, 0.9≤α≤1. It can be understood that the value of the integrity threshold is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as it satisfies the analysis of the inspection integrity. The optimal value of the integrity threshold is: α=0.95.

[0031] Specifically, in this embodiment, the inspection integrity analysis unit analyzes the missing values ​​and abnormal values ​​in the random self-inspection information to count the number of missing values ​​and abnormal values ​​in the initial self-inspection information, thereby judging the inspection integrity and realizing the judgment of whether the data content is complete, thereby improving the system's analysis efficiency of medical device inspection data and improving the accuracy of abnormality analysis of medical device inspection data.

[0032] Please continue reading Figure 2 As shown, the production data analysis module also includes: The sampling feature analysis unit is used to analyze the sampling operation characteristics according to the production inspection information, and the sampling feature analysis unit is connected to the inspection integrity analysis unit.

[0033] Specifically, the sampling feature analysis unit in this embodiment analyzes the sampling operation feature according to the sampling operation power and the rated power of the device, sets the sampling operation feature as C(p,i), and sets , where P1(p,i,t) represents the sampling operation power, Pe represents the rated power of the device, t represents the operation time number, T(p,i) represents the set of operation time numbers for the current analysis of the medical device sampling, and NT(p,i) represents the number of elements in the set of operation time numbers for the current analysis of the medical device sampling. The operation time number is defined as the number that distinguishes the data generated by the medical device every second.

[0034] Specifically, in this embodiment, the sampling feature analysis unit analyzes the sampling operating power and the rated power of the device to analyze the sampling operation characteristics, and the sampling operation characteristics are used to represent the analysis of whether the operating power of the medical device is stable when the medical device is sampled, thereby improving the system's analysis efficiency of medical device inspection data and improving the accuracy of abnormality analysis of medical device inspection data.

[0035] Please continue reading Figure 1 As shown, the medical device inspection data processing system also includes: The sampling storage and analysis module is used to store production inspection information and sampling operation characteristics, and analyze batch sampling characteristics. The sampling storage and analysis module is connected to the production data analysis module.

[0036] Specifically, the sampling storage and analysis module described in this embodiment regards medical devices with a qualified sampling status and incomplete inspection integrity data as Class I devices, medical devices with a qualified sampling status and complete inspection integrity data as Class II devices, and medical devices with unqualified sampling status as Class III devices, and analyzes batch sampling characteristics based on Class I devices, Class II devices, Class III devices and sampling operation characteristics, sets the batch sampling characteristics as B(p), and sets , where U1(p) represents the set of sampling device numbers corresponding to Class I devices, U2(p) represents the set of sampling device numbers corresponding to Class II devices, U3(p) represents the set of sampling device numbers corresponding to Class III devices, σ(C(p,i)) represents the standard deviation of the sampling operating power, and Avg(C(p,i)) represents the average value of the sampling operating power.

[0037] Specifically, in this embodiment, the sampling storage and analysis module analyzes the sampling status and inspection integrity to divide medical devices into three categories, thereby analyzing batch sampling characteristics, and using batch sampling characteristics to represent the operating characteristic relationship between different types of medical devices in the current batch of medical devices, thereby improving the system's analysis efficiency of medical device inspection data and improving the accuracy of abnormality analysis of medical device inspection data.

[0038] Please continue reading Figure 1 As shown, the medical device inspection data processing system also includes: The outbound usage analysis module is used to analyze the equipment usage characteristics and information verification parameters based on the outbound usage information and production inspection information. The information verification parameters include usage integrity and self-inspection information consistency. The outbound usage analysis module is connected to the inspection data acquisition module.

[0039] Specifically, in this embodiment, the outbound usage information and production inspection information are analyzed through the outbound usage module to analyze the device usage characteristics, usage integrity and self-inspection information consistency, so that the analysis of the inspection data is consistent with the data analysis process during random inspection, and whether the data of the medical device at the time of random inspection is consistent with the data of the medical device at the time of outbound use is analyzed, thereby improving the system's analysis efficiency of medical device inspection data and improving the accuracy of abnormality analysis of medical device inspection data.

[0040] See also Figure 3 As shown, the outbound usage analysis module includes: The usage characteristic analysis unit is used to analyze the usage characteristics of the equipment according to the outbound usage information.

[0041] Specifically, the usage characteristic analysis unit described in this embodiment analyzes the equipment usage characteristics according to the out-of-warehouse operating power and the rated power of the equipment, sets the equipment usage characteristics to D(p,i,t), and sets D(p,i,t)=P2(p,i,t) / Pe, where P2(p,i,t) represents the out-of-warehouse operating power.

[0042] Please continue reading Figure 3 As shown, the outbound usage analysis module also includes: The usage integrity analysis unit is used to analyze the usage integrity according to the outbound usage information, and the usage integrity analysis unit is connected to the usage feature analysis unit.

[0043] Specifically, the usage integrity analysis unit described in this embodiment determines whether the data in the usage self-check information is missing values ​​and abnormal values, so as to analyze the number of missing values, the number of abnormal values ​​and the number of data in the usage self-check information to determine the usage integrity. The judgment process is the same as the analysis process of checking the integrity of the random sampling self-check information, and will not be elaborated in this embodiment.

[0044] Please continue reading Figure 3 As shown, the outbound usage analysis module also includes: The self-inspection data analysis unit is used to analyze the consistency of the self-inspection information according to the outbound usage information and the production inspection information. The self-inspection data analysis unit is connected to the usage integrity analysis unit.

[0045] Specifically, the self-inspection data analysis unit described in this embodiment analyzes the consistency of the self-inspection information based on the outbound usage information and the production inspection information. The self-inspection data analysis unit determines whether the usage information type and the sampling information type are the same and whether the names of the data in the collected self-inspection content are the same. If the usage information type and the sampling information type are the same and the names of the data in the collected self-inspection content are the same, the self-inspection data analysis unit determines that the self-inspection information consistency is self-inspection consistent; if the usage information type and the sampling information type are not the same or the names of the data in the collected self-inspection content are not the same, the self-inspection data analysis unit determines that the self-inspection information consistency is self-inspection inconsistent.

[0046] Please continue reading Figure 1 As shown, the medical device inspection data processing system also includes: The outbound storage analysis module is used to store outbound usage information and equipment usage characteristics and analyze user levels. The outbound storage analysis module is connected to the outbound usage analysis module.

[0047] Specifically, the outbound storage analysis module in this embodiment analyzes the user operation characteristics according to the usage records and the device usage characteristics, sets the user operation characteristics as R(n), and sets , where UT(n) represents the time the current user operates the medical device in the usage record, NUT(n) represents the duration of the current user operating the medical device in the usage record, in seconds, and n represents the operating user number. The operating user number is defined as the number that distinguishes different operating users in the usage record.

[0048] Specifically, the outbound storage analysis module in this embodiment analyzes the user level according to the user operation characteristics. If |R(n)-1|≤r, the outbound storage analysis module determines that the user level is level one; if |R(n)-1|>r, the outbound storage analysis module determines that the user level is level two; wherein r represents the level threshold, 0.05≤r≤0.1. It can be understood that the value of the level threshold is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as it satisfies the analysis of the user level. The optimal value of the level threshold is: r=0.05.

[0049] Specifically, in this embodiment, the usage records and instrument usage characteristics are analyzed by the outbound storage analysis module to analyze the user operation characteristics, and the user operation characteristics are used to represent the operating data characteristics of the medical device when different users use the medical device, so as to divide the user levels, thereby improving the system's analysis efficiency of medical device inspection data and improving the accuracy of abnormality analysis of medical device inspection data.

[0050] Please continue reading Figure 1As shown, the medical device inspection data processing system also includes: The feature comprehensive analysis module is used to analyze the device outbound characteristics according to batch qualification parameters, batch sampling characteristics, usage integrity, self-inspection information consistency, device usage characteristics, user level and outbound usage information. The feature comprehensive analysis module is connected to the sampling storage analysis module and the outbound storage analysis module.

[0051] See also Figure 4 As shown, the feature comprehensive analysis module includes: The outbound feature analysis unit is used to analyze the outbound features of the equipment based on the batch sampling features, equipment usage features and batch qualification parameters.

[0052] Specifically, the outbound feature analysis unit in this embodiment analyzes the outbound feature of the device according to the batch sampling feature, the device usage feature and the batch qualified parameter, and sets the outbound feature of the device as F(p,i). , where Avg(D(p,i,t)) represents the average value of the device usage characteristics, and Ut represents the time corresponding to the current operation of the medical device.

[0053] Specifically, in this embodiment, the outbound feature analysis unit analyzes the batch sampling features, the instrument usage features and the batch qualification parameters to analyze the instrument outbound features. The instrument outbound features are used to represent the characteristic relationship between the operating data features of the medical instruments after they are outbound and used and the sampling data of the medical instruments in the same batch, thereby achieving a comprehensive analysis of the sampling data and outbound usage data, thereby improving the system's analysis efficiency of the medical instrument inspection data and improving the accuracy of the abnormality analysis of the medical instrument inspection data.

[0054] Please continue reading Figure 4 As shown, the feature comprehensive analysis module also includes: The user level analysis unit is used to analyze the user operation parameters according to the user level, usage records and instrument usage characteristics. The user level analysis unit is connected to the outbound feature analysis unit.

[0055] Specifically, the user level analysis unit in this embodiment analyzes the user operation parameters according to the user level, usage records and device usage characteristics, sets the user operation parameters as G(p,i), and sets , where L1 represents the operation time of a user whose user level is level one in the usage record, and L2 represents the operation time of a user whose user level is level two in the usage record.

[0056] Specifically, in this embodiment, the user level analysis unit analyzes the user level, usage records and instrument usage characteristics to analyze the user operation parameters, and uses the user operation parameters to represent the difference characteristics of the operating data when different user levels use medical instruments, thereby improving the system's analysis efficiency of medical instrument inspection data and improving the accuracy of abnormality analysis of medical instrument inspection data.

[0057] Please continue reading Figure 4 As shown, the feature comprehensive analysis module also includes: The operation association analysis unit is used to analyze the operation difference state according to the user operation parameters, and the operation association analysis unit is connected to the user level analysis unit.

[0058] Specifically, the operation association analysis unit in this embodiment analyzes the operation difference state according to the user operation parameters. If |G(p,i)-1|≤g, the operation association analysis unit determines that the operation difference state is a small difference; if |G(p,i)-1|>g, the operation association analysis unit determines that the operation difference state is a large difference, and processes the analysis process of the device outbound feature. The processed device outbound feature is F1(p,i), and F1(p,i)=F1(p,i) / e is set. |G(p,i)-1| ; Wherein, g represents the difference threshold, 0.05≤g≤0.1. It can be understood that the value of the difference threshold is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as it satisfies the analysis of the operation difference state. The optimal value of the difference threshold is: g=0.08.

[0059] Specifically, in this embodiment, the operation association analysis unit analyzes the user operation parameters to analyze the operation difference status, realizes the difference analysis between different operating users of the current instrument, and processes the instrument outbound characteristics when the difference is large, so that the processed instrument outbound characteristics are related to the operation differences of different users, thereby improving the system's analysis efficiency of medical device inspection data and improving the accuracy of abnormality analysis of medical device inspection data.

[0060] Please continue reading Figure 4 As shown, the feature comprehensive analysis module also includes: The self-check correlation analysis unit is used to analyze data security according to usage integrity and self-check information consistency, and the self-check correlation analysis unit is connected to the operation correlation analysis unit.

[0061] Specifically, the self-check association analysis unit described in this embodiment analyzes data security based on usage integrity and self-check information consistency. If the self-check information consistency is self-check consistent or the usage integrity is complete data, the self-check association analysis unit determines that the data is safe. If the usage integrity is incomplete data and the self-check information consistency is self-check inconsistent, the self-check association analysis unit determines that the data is unsafe and processes the analysis process of the user operation parameters. The processed user operation parameters are G1(p,i), and G1(p,i) is set to G(p,i)=G(p,i)×[ND1(p,i)+NLD(p,i)] / ND(p); wherein ND1(p,i) represents the number of missing uses, ND2(p,i) represents the number of abnormal uses, and ND(p) represents the number of data in the self-check information used.

[0062] Specifically, in this embodiment, the self-inspection association analysis unit analyzes the usage integrity and consistency of the self-inspection information to analyze data security, and realizes analysis of whether the operation inspection data of the medical device after leaving the warehouse is the same as that during the random inspection. In the case of data insecurity, the user operation parameters are processed to reduce the impact of the difference between the random inspection data and the warehouse usage data on the analysis of the device operation data, thereby improving the system's analysis efficiency of the medical device inspection data and improving the accuracy of the abnormality analysis of the medical device inspection data.

[0063] Please continue reading Figure 1 As shown, the medical device inspection data processing system also includes: The data anomaly processing module is used to analyze and output the anomaly of the medical device's outbound usage information based on the device's outbound characteristics.

[0064] Specifically, the data anomaly processing module described in this embodiment analyzes the abnormality of the outbound use information of the medical device according to the outbound characteristics of the device. If |F(p,i)-1|>f, the data anomaly processing module determines that the abnormality of the outbound use information of the medical device is data abnormality, and outputs the abnormality of the outbound use information to the user; if |F(p,i)-1|≤f, the data anomaly processing module determines that the abnormality of the outbound use information of the medical device is data normal; wherein f represents the abnormality judgment threshold, 0.1≤f≤0.15. It can be understood that the value of the abnormality judgment threshold is not specifically limited in this embodiment, and the technicians in this field can freely set it, as long as the judgment of the abnormality of the outbound use information is satisfied. The optimal value of the abnormality judgment threshold is: f=0.12.

[0065] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the embodiments here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. A medical device inspection data processing system, characterized in that: include: Inspection data collection module, used to collect production inspection information and outbound use information of medical devices; Production data analysis module, used to analyze batch qualification parameters, inspection integrity and sampling operation characteristics based on production inspection information; The sampling inspection storage and analysis module is used to store production inspection information and sampling inspection operation characteristics, and analyze batch sampling inspection characteristics; The outbound usage analysis module is used to analyze the device usage characteristics and information verification parameters based on the outbound usage information and production inspection information; The outbound storage analysis module stores outbound usage information and equipment usage characteristics, and analyzes user levels; The feature comprehensive analysis module is used to analyze the device delivery characteristics according to the batch sampling characteristics, device usage characteristics and batch qualified parameters, and is also used to analyze the user operation parameters according to the user level, delivery usage information and device usage characteristics, and to process the analysis process of the device delivery characteristics, and is also used to process the analysis process of the user operation parameters according to the information verification parameters; The data anomaly processing module is used to analyze and output the anomaly of the medical device's outbound usage information based on the device's outbound characteristics.

2. The medical device inspection data processing system according to claim 1, characterized in that: The production data analysis module is provided with a qualified parameter analysis unit, which is used to count the number of medical devices that are qualified in the random inspection status as the qualified number of random inspections, and analyze the batch qualified parameters according to the qualified number of random inspections NA1(p), the production batch p, the batch number NA3(p) and the random inspection number NA2(p) to obtain the batch qualified parameters A(p).

3. The medical device inspection data processing system according to claim 2, characterized in that: The production data analysis module is also provided with an inspection integrity analysis unit, which is used to count the number of data with missing values ​​in the random inspection self-inspection information as the random inspection missing number NL1(p,i), count the number of data with abnormal values ​​in the random inspection self-inspection information as the random inspection abnormal number NL2(p,i), and judge the inspection integrity based on the random inspection missing number and the random inspection abnormal number, and the inspection integrity includes complete data and incomplete data.

4. The medical device inspection data processing system according to claim 3, characterized in that: The production data analysis module is also provided with a sampling feature analysis unit, which is used to analyze the sampling operation characteristics according to the sampling operation power P1 (p, i, t) and the equipment rated power Pe to obtain the sampling operation characteristics C (p, i).

5. The medical device inspection data processing system according to claim 4, characterized in that: The sampling storage and analysis module classifies medical devices with a qualified sampling status and incomplete inspection integrity data as Class I devices, medical devices with a qualified sampling status and complete inspection integrity data as Class II devices, and medical devices with an unqualified sampling status as Class III devices, and analyzes batch sampling characteristics based on Class I devices, Class II devices, Class III devices and sampling operation characteristics to obtain batch sampling characteristics B(p).

6. The medical device inspection data processing system according to claim 5, characterized in that: The outbound storage analysis module analyzes the user operation characteristics according to the usage records and the device usage characteristics to obtain the user operation characteristics as R(n); The outbound storage analysis module analyzes the user level according to the user operation feature R(n) to obtain the user level, and the user level includes the first level and the second level.

7. The medical device inspection data processing system according to claim 6, characterized in that: The feature comprehensive analysis module is provided with a warehouse-out feature analysis unit, which is used to analyze the equipment warehouse-out feature according to the batch sampling feature B(p), the equipment usage feature D(p,i,t) and the batch qualified parameter A(p) to obtain the equipment warehouse-out feature F(p,i).

8. The medical device inspection data processing system according to claim 7, characterized in that: The feature comprehensive analysis module is also provided with a user level analysis unit, which is used to analyze the user operation parameters according to the user level, usage records and device usage characteristics to obtain the user operation parameters G(p,i); The feature comprehensive analysis module is also provided with an operation association analysis unit, which is used to analyze the operation difference state according to the user operation parameters to obtain the operation difference state, wherein the operation difference state includes a large difference and a small difference, and when the operation difference state is a large difference, the analysis process of the equipment outbound feature is processed, and the processed equipment outbound feature is F1(p,i).

9. The medical device inspection data processing system according to claim 8, characterized in that: The feature comprehensive analysis module is also provided with a self-check correlation analysis unit, which is used to analyze data security based on usage integrity and self-check information consistency. The data security includes data security and data insecurity. When the data is insecure, the analysis process of the user operation parameters is processed, and the processed user operation parameters are G1(p,i).

10. The medical device inspection data processing system according to claim 9, characterized in that: The data anomaly processing module analyzes the abnormality of the medical device's outbound usage information according to the device outbound characteristics to obtain the abnormality of the outbound usage information, wherein the abnormality of the outbound usage information includes normal data and abnormal data, and when the abnormality of the outbound usage information is data abnormality, the abnormality of the outbound usage information is output to the user.

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